Reviewerly/exHarmony huggingface dataset:
The Best Features Selection Method and Relevance Variable for Web Phishing Classification

The application of data mining using high dimensional dataset can cause problem in accuracy and time consumption in the classification phase. One of the solutions is to reduce the dimension of the dataset using feature selection. This research aims to select the appropriate feature selection for the classification process and find out whether feature selection can improve accuracy or not. The feature selection methods that are often used are information gain, gain ratio, chi-square, and correlation-based feature selection. This research will be done a comparison of the four feature selection methods techniques to determine whether the feature selection process can always increase the accuracy and reduce the computation time of the classification algorithm or not. According to the research that has been done, applying the four methods can make the accuracy of NB, K-NN, SVM, DT, and ID3 algorithm decreases. However, it reduces the computation time of K-NN, SVM, and ID3 algorithms. Using the four feature selection methods, the most influential attributes are SSLfinal_State, Having_sub_domain,URL_of_Anchor,Preffix_suffix,SFH,Dom ain_registration_length, Links_intags, Web_traffic, Request_url, and Google_index. According to these results, the feature selection process can decrease the accuracy of the classification algorithm. This is due to either the character of the data or the classification algorithm itself. The feature selection process can also reduce computing time so that speed up the working process of the classification algorithm used. This is because of the data dimensions are getting smaller.
Distributed Kalman Filter for Multitarget Tracking Systems With Coupled Measurements

In multitarget tracking systems, it is usually assumed that each measurement is generated with respect to a single target. This is not always true for generating relative state measurements or cross-target information in a coupled fashion. This note is concerned with the problem of distributed filtering for multitarget tracking systems with coupled measurements. By representing the coupling features of the target states in the measurements as a directed graph, a modified Kalman consensus filter (KCF) is proposed for a target-dependent augmented system whose state vector consists of in-going neighborhood targets. To analyze the performance of the modified KCF in a directed graph, a sufficient condition is derived to guarantee the boundedness of the estimation errors in the mean square sense. Numerical studies are provided to verify the applicability of the KCF.
An Effective Approach for the Protection of User Privacy in a Digital Library

Abstract In a digital library, an increasingly important problem is how to prevent the exposure of user privacy in an untrusted network. This study aims to design an effective approach for the protection of user privacy in a digital library, by consulting the basic ideas of encryption and anonymization. In our proposed approach, any privacy data, which can identify user’s real identity, should be encrypted first before being submitted to the library server, to achieve anonymization of user identity. Then, to solve the problem of querying encrypted privacy data, additional feature data are constructed for the encrypted data, such that much of the query processing can be completed at the server-side, without decrypting the data, thereby improving the efficiency of each kind of user query operation. Both theoretical analysis and experimental evaluation demonstrate the effectiveness of the approach, which can improve the security of users’ data privacy and behavior privacy on the untrusted server-side, without compromising the availability (i. e. accuracy, efficiency, and usability) of digital library services. This paper provides a valuable study attempt at the protection of digital library users’ privacy, which has a positive influence on the development of a privacy-preserving library in an untrusted network environment.
Malicious-URL Detection using Logistic Regression Technique

Over the last few years, the Web has seen a massive growth in the number and kinds of web services. Web facilities such as online banking, gaming, and social networking have promptly evolved as has the faith upon them by people to perform daily tasks. As a result, a large amount of information is uploaded on a daily to the Web. As these web services drive new opportunities for people to interact, they also create new opportunities for criminals. URLs are launch pads for any web attacks such that any malicious intention user can steal the identity of the legal person by sending the malicious URL. Malicious URLs are a keystone of Internet illegitimate activities. The dangers of these sites have created a mandates for defences that protect end-users from visiting them. The proposed approach is that classifies URLs automatically by using Machine-Learning algorithm called logistic regression that is used to binary classification. The classifiers achieves 97% accuracy by learning phishing URLs.
Influence of the "Ghost Reed" Simplification on the Bifurcation Diagram of a Saxophone Model

This paper presents how the bifurcation diagram of a saxophone model is affected by the contact force limiting the displacement of the reed when it strikes the mouthpiece lay. The reed impact is modeled by a nonlinear stiffness and damping activated by contact with the lay. The impact model is compared with the "ghost reed" simplification, where the reed moves through the lay unimpeded. Bifurcation diagrams in both cases are compared, in terms of amplitude of the oscillations and location of the bifurcations, on the solution branches corresponding to the first and second register. The ghost reed simplification has limited influence at low values of the blowing pressure parameter: the diagrams are similar. This is true even for "beating reed" regimes, in which the reed coincides with the lay. The most noticeable discrepancies occur near the extinction of the oscillations, at high blowing pressure.
ROLE OF ENVIRONMENTAL AWARENESS IN THE APPLICATION OF ENVIRONMENTAL ACCOUNTING DISCLOSURE IN TOURISM AND HOTEL COMPANIES AND ITS IMPACT ON INVESTOR’S DECISIONS IN AMMAN STOCK EXCHANGE

The present study aims at examining the level of environmental awareness in the management of tourism and hotels companies listed in the Amman Stock Exchange, its role in the application of environmental accounting disclosure, and to examine the impact of investment decisions on environmental accounting disclosure. To achieve this objective, a sample is chosen from two categories: tourism and hotels management category which consists of 12 financial managers and a major accountant in those companies, and the category of investors in these industrial companies which consists of 47 investors are randomly selected. In order to collect the data, a questionnaire is distributed to each of the hospitality companies and investors in these companies. The study shows that there is a good level of environmental awareness among the management of companies. However, there is a fear of applying the environmental accounting disclosure due to the lack of certainty of the importance of application and the benefits of it, and the lack of appropriate qualification of accountants to apply environmental accounting disclosure. The study shows that there is a real presence of environmental legislation, but there is a large deficiency in the activation of such legislation. Keywords: Environmental Accounting, Amman Stock Exchange, environmental awareness , JEL Classifications: Q56,R 50,Q0 DOI: https://doi.org/10.32479/ijeep.8608
Does Awareness of Social Engineering Make Employees More Secure?

Social engineering has become one of the biggest security threats facing organizations.Rather than relying upon information security technical-related shortcomings to break into computer networks, social engineers make use of employees' individual and organizational traits to deceive them.In such a scenario, it is crucial for organizations to ensure that their employees not only possess sound knowledge about information security but also about the concept of social engineering and threats emerging from social engineering attacks.This study aims to test whether awareness of social engineering can predict and explain individuals' securityprotective practices.We conducted a survey of 265 employees working in different organizations in Saudi Arabia.The results suggest that awareness of social engineering is a positive predictor of security-protective practices above and beyond the predictability power of possessing information security knowledge.Thus, to reduce the probability of potential consequences of social engineering attacks, our study suggests that organizations should not only strive to enhance employees' security knowledge but should also invest in increasing employees' awareness of social engineering.
Learning Robust Shape-Based Features for Domain Generalization

Domain generalization is a challenging problem of learning models that can generalize to novel testing domains which are unavailable during training and follow different distributions from training domains.In this paper, we introduce a simple but effective method for domain generalization, which is based on the object shape hypothesis.The main idea is from some recent studies which demonstrate that leading the network to focus more on the object shape and recognizing objects through shape-based features are more significant and robust.To achieve this, we first stylize source domain images into randomly sampled styles through a style transfer network and then use both the stylized images and original source domain images to learn the classification model.With images of the same content but different styles (textures), we expect the network to classify according to the global object shape rather than local textures and further transfer the object shape knowledge to novel domains.By this way, the generalization ability of the classification model will be improved.We conduct extensive experiments on PACS, VLCS and OfficeHome benchmarks and both the quantitative and qualitative analysis demonstrate the effectiveness of the proposed method.
Adaptation of RNN Transducer with Text-To-Speech Technology for Keyword Spotting

With the advent of recurrent neural network transducer (RNN-T) model, the performance of keyword spotting (KWS) systems has greatly improved. However, the KWS systems, employed for wake-word detection, still rely on the availability of keyword specific training data for achieving reasonable performance on each keyword. With a goal to improve the KWS performance for these keywords without having to collect additional natural speech data, we explore Text-To-Speech (TTS) technology to synthetically generate training data for such keywords. Employing an RNN-T based KWS model, already well trained on large keyword-independent natural speech dataset, as a seed model, we run adaptation experiments using the generated keyword-specific TTS data. Besides observing a considerable improvement in the overall performance for the low-resource keywords, we find that the performance improvement with TTS-generated training data, similar to natural speech data, depends on speaker diversity, amount of data per speaker and data simulation. We get additional improvement in performance by selectively adapting specific parts of the RNN-T model and gain key insights into different architectural constructs of RNN-T model.
Ensuring sustainable development of education of future maritime transport professionals by means of network interaction

The article describes the concept of sustainable development, which is one of the modern trends of scientific research in the 21st century. The analysis of scientific researches has been carried out in the context of the mentioned problem of sustainable development, the tendencies of development of the network interaction theory as factor of sustainable development of maritime education have been transformed into pedagogical practice of maritime higher educational establishments. The main objective of the article is to express the idea that the rapid development of information is the main impetus for the transformation of education, updating its content and forms, methods and technologies, improving the efficiency and accessibility of quality education. The process of network interaction on the example of creating an e-course for foreign students is substantiated. There was considered the possibility of modernizing the training of maritime professionals using modern information technologies (distance technologies, augmented reality and VR). The prospect of network interaction ideas has been proved, which is confirmed by the effectiveness of the introduction of the Moodle e-course for international entrants. It is shown that the students participate in network interaction, create the frame for successful adaptation of international students in Ukraine.
Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.

Surgical Site Infection surveillance in healthcare systems is labor intensive and plagued by underreporting as current methodology relies heavily on manual chart review. The rapid adoption of electronic health records (EHRs) has the potential to allow the secondary use of EHR data for quality surveillance programs. This study aims to investigate the effectiveness of integrating natural language processing (NLP) outputs with structured EHR data to build machine learning models for SSI identification using real-world clinical data. We examined a set of models using structured data with and without NLP document-level, mention-level, and keyword features. The top-performing model was based on a Random Forest classifier enhanced with NLP document-level features achieving a 0.58 sensitivity, 0.97 specificity, 0.54 PPV, 0.98 NPV, and 0.52 F0.5 score. We further interrogated the feature contributions, analyzed the errors, and discussed future directions.
Pyramid Matting: A Resource-Adaptive Multi-Scale Pixel Pair Optimization Framework for Image Matting

Image matting is an important problem in computer vision with significant theoretical interest and diverse practical applications, including image/video editing, target tracking, and object recognition. Pixel-pair-optimization-based image matting approaches have been shown very successful in estimating the opacity of the foreground by searching for the best pair of foreground and background pixels for each unknown pixel. However, extant approaches encounter difficulties in adapting to the changes of available computing resources, which limits the application of image matting. This drawback has motivated the present study, as a part of which a multi-scale evolutionary pixel pair optimization framework named pyramid matting framework (PMF) was developed. In this framework, the large-scale pixel pair optimization problem is transformed to multiple pixel pair optimization problems of different scales using image pyramid. The resulting problems are solved level by level, starting from the problem at the small scale. Pixel pair heuristic information obtained from solving low-scale problems are iteratively propagated to the spatially-related pixel pairs in the larger-scale problem. PMF can adapt to changes in available computing resources due to its capability of transforming a small-scale problem solution to the large-scale problem solution through the heuristic information propagation. Experimental results show that the PMF-based image matting approach not only provides high-quality alpha mattes with sufficient computing resources, but also works well when computing resources are scarce.
Gaze'N'Touch: Enhancing Text Selection on Mobile Devices Using Gaze

Text selection is a frequent task we do everyday to edit, modify or delete text. Selecting a word requires not only precision but also switching between selections and typing which influences both speed and error rates. In this paper, we evaluate a novel concept that extends text editing with an additional modality, that is gaze. We present a user study (N=16) where we explore how, the novel concepts called GazeButton can improve text selection by comparing it to touch based selection. In addition, we tested the effect of text size on the selection techniques by comparing two different text sizes.Results show that gaze based selection was faster with bigger text size, although not statistically significant. Qualitative feedback show a preference on gaze over touch which motivates a new direction of gaze usage in text editors.
Probabilistic regressor chains with Monte Carlo methods

A large number and diversity of techniques have been offered in the literature in recent years for solving multi-label classification tasks, including classifier chains where predictions are cascaded to other models as additional features. Chaining methods have often providing state of the art results, and the idea of extending it to multi-output regression has already been trialed. However, these 'regressor chains' have seen limited applicability, on account of yielding relatively little predictive performance compared to individual regression models, and also limited interpretability. In this work we identify and discuss the main limitations of regressor chains, including an analysis of different base models, loss functions, explainability, and other desiderata of real-world applications. We develop and examine techniques to overcome these limitations. In particular we present Monte Carlo schemes in the framework of probabilistic chains. We show they can be effective, flexible and useful in different areas. Overall, we also place regressor chains in context among general multi-output learning with continuous outputs, and in doing this shed additional light on the applicability of chaining to machine learning tasks.
Enabling the Reuse of Software Development Assets Through a Taxonomy for User Stories

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context</i> - Agile Software Development (ASD) and Reuse-Driven Software Engineering (RDSE) are well-accepted strategies to improve the efficiency of software processes. A challenge to integrate both approaches is that ASD relies mostly on tacit knowledge, hampering the reuse of software development assets. An opportunity to enable RDSE for ASD is by improving the traceability between user stories (USs), the most used notation to register product requirements in ASD. Having enough link semantics between USs could enable defining similarity between them and, consequently, promote RDSE for ASD. However, this is an open challenge. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective</i> - To propose a taxonomy for adding link semantics between USs, focusing on easing the task of identifying similar ones. Such links, with support of traceability tools, enable the reuse of USs and their related assets. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Method</i> : We constructed a taxonomy for types of US focusing on Web Information Systems. The taxonomy is used to classify the US, given two facets: module and operation. Such information is used to infer the similarity between USs using link rules. We developed the taxonomy based on an empirical analysis of five product backlogs, containing a total of 118 USs. Afterward, we validated the taxonomy in terms of its potential to enable the reuse of US-related assets. First, we executed an offline validation by applying it to classify 530 USs from 26 already ended projects. Finally, we applied the taxonomy in a case study with two ongoing projects (59 USs). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results</i> : The proposed taxonomy for USs is composed of two sub-facets, namely, module and operation, which have, respectively, three and 18 terms. In terms of coverage, for the offline study and case study, we classified 90.17% of the USs with the proposed taxonomy. For the case study, we classified all the USs analyzed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion</i> : We concluded that it is possible to use our approach to compare USs and, consequently, retrieve their related assets. Our results regarding its practical utility have shown that users considered the taxonomy a useful approach to ease the process of assessing the similarity between user stories.
Panoramic dual-directional shearography assisted by a bi-mirror

A panoramic dual-directional shearography system is proposed to simultaneously determine out-of-plane deformation derivatives in two directions and globally inspect the object to be tested. A dichroic filter (DF), a 3CCD camera, and dual-wavelength light are used in the proposed shearography configuration. The dual-wavelength light coupled with the corresponding imaging sensors of the 3CCD camera provides independent color signals and shearograms. Through adjustment of the tilted stereo-angle of the DF, which offers a second wavelength-dependent measurement, an additional independent image-shearing can be introduced into the setup. The auxiliary bi-mirror surrounding the object helps to fully illuminate the object surface and capture it in a single shot. Theoretical analysis and experimental results demonstrated the utility of the system.
AIRA Chatbot for Travel: Case Study of AirAsia

Abstract Tourism is one of the main economic contribution to many countries worldwide. This paper presents an Artificial Intelligence tool to help improve the performance, quality and credibility of customer service for AirAsia Berhad, a renowned local business in travel/airline industry in Malaysia. The tool, AIRA, is developed using C# in Verbot 5.0 and plays an important role as an information gatherer, gathers all the latest and correct information in order to provide the best service to customers. The evaluation has proved that AIRA provided a simple, easy to navigate GUI with high efficiency, hence a user-friendly system that requires little or no technical knowledge to operate. AIRA is hoped to improve the quality of customer service in AirAsia by giving an alternative to entertain travellers.
A New Composite Fractal Function and Its Application in Image Encryption

Fractal’s spatially nonuniform phenomena and chaotic nature highlight the function utilization in fractal cryptographic applications. This paper proposes a new composite fractal function (CFF) that combines two different Mandelbrot set (MS) functions with one control parameter. The CFF simulation results demonstrate that the given map has high initial value sensitivity, complex structure, wider chaotic region, and more complicated dynamical behavior. By considering the chaotic properties of a fractal, an image encryption algorithm using a fractal-based pixel permutation and substitution is proposed. The process starts by scrambling the plain image pixel positions using the Henon map so that an intruder fails to obtain the original image even after deducing the standard confusion-diffusion process. The permutation phase uses a Z-scanned random fractal matrix to shuffle the scrambled image pixel. Further, two different fractal sequences of complex numbers are generated using the same function i.e. CFF. The complex sequences are thus modified to a double datatype matrix and used to diffuse the scrambled pixels in a row-wise and column-wise manner, separately. Security and performance analysis results confirm the reliability, high-security level, and robustness of the proposed algorithm against various attacks, including brute-force attack, known/chosen-plaintext attack, differential attack, and occlusion attack.