Reviewerly/exHarmony huggingface dataset:
Detection of XSS Attacks in Web Applications: A Machine Learning Approach

With the increased use of the internet, web applications and websites are becoming more and more common.With the increased use, cyber-attacks on web applications and websites are also increasing.Of all the different types of cyber-attacks on web applications and websites, XSS (Cross-Site Scripting) attacks are one of the most common forms of attack.XSS attacks are a major problem in web security and ranked as number two web application security risks in the OWASP (Open Web Application Security Project) Top 10.Traditional methods of defence against XSS attacks include hardware and software-based web application firewalls, most of which are rule and signature-based.Rule-based and signature-based web application firewalls can be bypassed by obfuscating the attack payloads.As such, rule-based and signaturebased web application firewalls are not effective against detecting XSS attacks for payloads designed to bypass web application firewalls.This paper aims to use machine learning to detect XSS attacks using various ML (machine learning) algorithms and to compare the performance of the algorithms in detecting XSS attacks in web applications and websites.
Artificial Intelligence: Implication on Management

The advancement in Artificial Intelligence (AI) is surging and it is at the center of improved output. Consequent to the advancement in the science of AI, human-to-machine interactions are facilitated, business models and logic now vary, and the lifestyle, as well as the living standards of humans, has changed. Artificial Intelligence (AI) is often regarded as a complex unit. There are many descriptions and numerous more impressions of what makes up Artificial Intelligence. The risk implications of Artificial Intelligence vary and it’s dependent on the degree of its usage. The advantages of artificial intelligence outnumber the risks it conveys, particularly in the world of business usage and its application is resulting in a world that is innovative and smarter. With the expectation that professions will be impacted, it conveys immediate effect on firms, their workers, and organizational management. The current study sought to answer some research questions; Is there knowledge about the effects of Artificial Intelligence on management? What is the current prediction for the future of management as it relates to the emergence of Artificial Intelligence? Does the current study specify the duties of middle managers in the future following the rise in AI? It adopted the systematic literature review approach where relevant literature is selected and used for the analysis. Following Jesson, Matheson &amp; Lacey (2011) approach to systematic review to include; (a) Mapping the area through a scoping review, b) Detailed domain search, c) Quality appraisal, d) Data gathering e) Synthesis, and f) Report writing, over 100 papers on the subject were selected. On screening, it was reduced to about 15 papers with a direct relationship on the subject. The study found out that; AI is a necessary evil to and for the future of firms. It will have varying degrees of effect on management. AI technology will discharge managerial assignments and will be helpful in addressing management challenges. Managers and management, therefore, need to get new competencies and skills to tap into new frontiers that are AI-based.
Ten simple rules for starting (and sustaining) an academic data science initiative

Data science has emerged as a new paradigm for research. Readers of this journal might be tempted to say this is the research we have been doing all along. However, we contest that there is something fundamentally different in terms of the dimensions of data, diversity of disciplines, as well as the role of the private sector, than what has gone before. We take this position based on our collective experiences in, and observations of, calls to action around data science over the past 10 years. Those calls have resulted in many notable and successful responses from US universities.A working definition of data scienceDefining data science is like defining the internet—ask 10 people and you get 10 different answers. What most would likely agree on, at a high level of abstraction, is that it draws from statistics, computer science, and applied mathematics to operate on data from one or more domains leading to outcomes not achieved otherwise. The extent to which domain knowledge is incorporated in the work of data science varies, but it is essential for achieving meaningful outcomes. Outcomes that have implications to us as humans and our collective communities and society that, in turn, need to be addressed as part of the data life cycle [1]. In short, data science transcends traditional disciplinary boundaries to discover new insights not owned by any one existing discipline, driven by endless streams of digital data with the promise of translation to societal benefit.
Activation Anomaly Analysis

Inspired by recent advances in coverage-guided analysis of neural networks, we propose a novel anomaly detection method. We show that the hidden activation values contain information useful to distinguish between normal and anomalous samples. Our approach combines three neural networks in a purely data-driven end-to-end model. Based on the activation values in the target network, the alarm network decides if the given sample is normal. Thanks to the anomaly network, our method even works in strict semi-supervised settings. Strong anomaly detection results are achieved on common data sets surpassing current baseline methods. Our semi-supervised anomaly detection method allows to inspect large amounts of data for anomalies across various applications.
Implementation of the NHI (Normalized Hot Spot Indices) Algorithm on Infrared ASTER Data: Results and Future Perspectives

The Normalized Hotspot Indices (NHI) tool is a Google Earth Engine (GEE)-App developed to investigate and map worldwide volcanic thermal anomalies in daylight conditions, using shortwave infrared (SWIR) and near infrared (NIR) data from the Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel 2 and Landsat 8 satellites. The NHI tool offers the possibility of ingesting data from other sensors. In this direction, we tested the NHI algorithm for the first time on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. In this study, we show the results of this preliminary implementation, achieved investigating the Kilauea (Hawaii, USA), Klyuchevskoy (Kamchatka; Russia), Shishaldin (Alaska; USA), and Telica (Nicaragua) thermal activities of March 2000–2008. We assessed the NHI detections through comparison with the ASTER Volcano Archive (AVA), the manual inspection of satellite imagery, and the information from volcanological reports. Results show that NHI integrated the AVA observations, with a percentage of unique thermal anomaly detections ranging between 8.8% (at Kilauea) and 100% (at Shishaldin). These results demonstrate the successful NHI exportability to ASTER data acquired before the failure of SWIR subsystem. The full ingestion of the ASTER data collection, available in GEE, within the NHI tool allows us to develop a suite of multi-platform satellite observations, including thermal anomaly products from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+), which could support the investigation of active volcanoes from space, complementing information from other systems.
Integrating Grasshopper Optimization Algorithm with Local Search for Solving Data Clustering Problems

This paper proposes a hybrid approach for solving data clustering problems.This hybrid approach used one of the swarm intelligence algorithms (SIAs): grasshopper optimization algorithm (GOA) due to its robustness and effectiveness in solving optimization problems.In addition, a local search (LS) strategy is applied to enhance the solution quality and access to optimal data clustering.The proposed algorithm is divided into two stages, the first of which aims to use GOA to prevent getting trapped in local minima and to find an approximate solution.While the second stage aims by LS to increase LS performance and obtain the best optimal solution.In other words, the proposed algorithm combines the exploitation capability of GOA and the discovery capability of LS, and integrates the merits of both GOA and LS.In addition, 7 well-known datasets that commonly used in several studies are used to validate the proposed technique.The results of the proposed methodology are compared to previous studies; where statistical analysis, for the various algorithms, indicated the superiority of the proposed methodology over other algorithms and its ability to solve this type of problem.
Applying al-zughair transform on nuclear physics

Al-Zughair transform is a novel transformation that is proposed in (2017), and due to its novelty it has not been applied to engineering fields, however Al-Zughair transform is capable to solve differential equations, for that reason it can be applied into engineering applications that include differential equations. In nuclear physics the radioactive decay of the atoms is well study subject, where there are many methods that are used to deal with it, however Al-Zughair transform has never been used before in that field, in this paper the differential equation of radioactive decay has been solved using Al-Zughair transform.Â Â
Do You See What I See? Effectiveness of 360-Degree vs. 2D Video Ads Using a Neuroscience Approach

This study compares cognitive and emotional responses to 360-degree vs. static (2D) videos in terms of visual attention, brand recognition, engagement of the prefrontal cortex, and emotions. Hypotheses are proposed based on the interactivity literature, cognitive overload, advertising response model and motivation, opportunity, and ability theoretical frameworks, and tested using neurophysiological tools: electroencephalography, eye-tracking, electrodermal activity, and facial coding. The results revealed that gaze view depends on ad content, visual attention paid being lower in 360-degree FMCG ads than in 2D ads. Brand logo recognition is lower in 360-degree ads than in 2D video ads. Overall, 360-degree ads for durable products increase positive emotions, which carries the risk of non-exposure to some of the ad content. In testing four ads for durable goods and fast-moving consumer goods (FMCG) this research explains the mechanism through which 360-degree video ads outperform standard versions.
Discovering Students’ Strategies in Learning English Online

Online learning poses challenges that students might never have encountered in a face-to-face learning environment. In learning English, students may confront more challenges as they need both cognitive and metacognitive skills in dealing with the dynamic lessons involving interaction, online exercises, and audio, video and text downloads. The challenges faced in online learning have led students to employ learning strategies to help them learn more efficiently and effectively. This paper examined students’ strategy use in learning English online and the correlation of the strategies with their academic performance in the subject. Using the Online Language Learning Strategy Questionnaire (OLLSQ) to gauge students’ strategy use in the domains of cognitive, metacognitive, resource-management and affective, the findings indicated that all students were high users of OLLS in English online learning with the highest preference for metacognitive as the strategies were helpful to students in planning and organizing their studies. However, there was low correlation between the strategies use and performance. Overall, the strategies have impacted the students positively and helped them to cope with the new learning mode that is different from the traditional learning. It is hoped that the discovery of the strategies could provide some important insights into how students can be more successful in learning online, and help others to achieve their study goals and overcome any challenges confronting them in learning English online.&#x0D; &#x0D; Keywords: E-learners, Learning strategies, Online learning, Performance
Optimal Ate Pairing on Elliptic Curves with Embedding Degree $9,15$ and $27$

Much attention has been given to the efficient computation of pairings on elliptic curves with even embedding degree since the advent of pairing-based cryptography. The few existing works in the case of odd embedding degrees require some improvements. This paper considers the computation of optimal ate pairings on elliptic curves of embedding degrees $k=9$, $15$, $27$ which have twists of order three. Our main goal is to provide a detailed arithmetic and cost estimation of operations in the tower extensions field of the corresponding extension fields. A good selection of parameters enables us to improve the theoretical cost for the Miller step and the final exponentiation using the lattice-based method as compared to the previous few works that exist in these cases. In particular, for $k=15$, $k=27$, we obtain an improvement, in terms of operations in the base field, of up to 25% and 29% respectively in the computation of the final exponentiation. We also find that elliptic curves with embedding degree $k=15$ present faster results than BN12 curves at the 128-bit security level. We provide a MAGMA implementation in each case to ensure the correctness of the formulas used in this work.
Ordered Fuzzy Numbers Applied in Bee Swarm Optimization Systems

The paper presents an innovative OFNBee optimization method based on combining the swarm intelligence with the use of directed fuzzy numers OFN. In the introduction, the issues related to the subject of the study, including bee algorithms and OFN numbers, were reviewed. The innovative OFNBee algorithm was presented and verified against a set of known benchmarks functions such as Sphere, Rastrigin, Griewank, Rosenbrock, Schwefel and Ackley. These functions have been applied due to their reliability in the literature. In the further part of the study, the configuration of the algorithm parameters is carried out, including the launch of each mathematical function several dozen times for different data, such as different population sizes. The key part of the research and analysis was to compare OFNBee with six standard ABC, MBO, IMBO, TLBO, HBMO, BBMO bee algorithms. The article ends with a summary and an indication of the possible future works.
Learning Accurate and Interpretable Decision Rule Sets from Neural Networks

This paper proposes a new paradigm for learning a set of independent logical rules in disjunctive normal form as an interpretable model for classification. We consider the problem of learning an interpretable decision rule set as training a neural network in a specific, yet very simple two-layer architecture. Each neuron in the first layer directly maps to an interpretable if-then rule after training, and the output neuron in the second layer directly maps to a disjunction of the first layer rules to form the decision rule set. Our representation of neurons in this first rules layer enables us to encode both the positive and the negative association of features in a decision rule. State-of-the-art neural net training approaches can be leveraged for learning highly accurate classification models. Moreover, we propose a sparsity-based regularization approach to balance between classification accuracy and the simplicity of the derived rules. Our experimental results show that our method can generate more accurate decision rule sets than other state-of-the-art rule-learning algorithms with better accuracy-simplicity trade-offs. Further, when compared with uninterpretable black-box machine learning approaches such as random forests and full-precision deep neural networks, our approach can easily find interpretable decision rule sets that have comparable predictive performance.
Ciphertext-policy attribute-based encryption with hidden sensitive policy from keyword search techniques in smart city

Abstract Countless data generated in Smart city may contain private and sensitive information and should be protected from unauthorized users. The data can be encrypted by Attribute-based encryption (CP-ABE), which allows encrypter to specify access policies in the ciphertext. But, traditional CP-ABE schemes are limited because of two shortages: the access policy is public i.e., privacy exposed; the decryption time is linear with the complexity of policy, i.e., huge computational overheads. In this work, we introduce a novel method to protect the privacy of CP-ABE scheme by keyword search (KS) techniques. In detail, we define a new security model called chosen sensitive policy security : two access policies embedded in the ciphertext, one is public and the other is sensitive and hidden. If user's attributes don't satisfy the public policy, he/she cannot get any information (attribute name and its values) of the hidden one. Previous CP-ABE schemes with hidden policy only work on the “AND-gate” access structure or their ciphertext size or decryption time maybe super-polynomial. Our scheme is more expressive and compact. Since, IoT devices spread all over the smart city, so the computational overhead of encryption and decryption can be shifted to third parties. Therefore, our scheme is more applicable to resource-constrained users. We prove our scheme to be selective secure under the decisional bilinear Diffie-Hellman (DBDH) assumption.
Establishment and Clinical Application of an Artificial Intelligence Diagnostic Platform for Identifying Rectal Cancer Tumor Budding

Tumor budding is considered a sign of cancer cell activity and the first step of tumor metastasis. This study aimed to establish an automatic diagnostic platform for rectal cancer budding pathology by training a Faster region-based convolutional neural network (F-R-CNN) on the pathological images of rectal cancer budding. Postoperative pathological section images of 236 patients with rectal cancer from the Affiliated Hospital of Qingdao University, China, taken from January 2015 to January 2017 were used in the analysis. The tumor site was labeled in Label image software. The images of the learning set were trained using Faster R-CNN to establish an automatic diagnostic platform for tumor budding pathology analysis. The images of the test set were used to verify the learning outcome. The diagnostic platform was evaluated through the receiver operating characteristic (ROC) curve. Through training on pathological images of tumor budding, an automatic diagnostic platform for rectal cancer budding pathology was preliminarily established. The precision–recall curves were generated for the precision and recall of the nodule category in the training set. The area under the curve = 0.7414, which indicated that the training of Faster R-CNN was effective. The validation in the validation set yielded an area under the ROC curve of 0.88, indicating that the established artificial intelligence platform performed well at the pathological diagnosis of tumor budding. The established Faster R-CNN deep neural network platform for the pathological diagnosis of rectal cancer tumor budding can help pathologists make more efficient and accurate pathological diagnoses.
Emotion Monitoring for Preschool Children Based on Face Recognition and Emotion Recognition Algorithms

In this paper, we study the face recognition and emotion recognition algorithms to monitor the emotions of preschool children. For previous emotion recognition focusing on faces, we propose to obtain more comprehensive information from faces, gestures, and contexts. Using the deep learning approach, we design a more lightweight network structure to reduce the number of parameters and save computational resources. There are not only innovations in applications, but also algorithmic enhancements. And face annotation is performed on the dataset, while a hierarchical sampling method is designed to alleviate the data imbalance phenomenon that exists in the dataset. A new feature descriptor, called “oriented gradient histogram from three orthogonal planes,” is proposed to characterize facial appearance variations. A new efficient geometric feature is also proposed to capture facial contour variations, and the role of audio methods in emotion recognition is explored. Multifeature fusion can be used to optimally combine different features. The experimental results show that the method is very effective compared to other recent methods in dealing with facial expression recognition problems about videos in both laboratory-controlled environments and outdoor environments. The method performed experiments on expression detection in a facial expression database. The experimental results are compared with data from previous studies and demonstrate the effectiveness of the proposed new method.
PENERAPAN NAÏVE BAYES CLASSIFIER UNTUK PENDUKUNG KEPUTUSAN PENERIMA BEASISWA

Scholarships are the provision of assistance in the form of financial assistance provided to individuals with the aim of being used for the sustainability of the education achieved. The problem that occurs in this research is that the process of determining which is still carried out conventionally the student section must check one by one the scholarship application files submitted by students because each data will be compared one by one according to predetermined criteria, which results in the student section becoming difficult in the decision so that It takes a long time, therefore we need a decision support system that can help schools make decisions about scholarship recipients.The Naive Bayes Classifier method is a method that can be used in decision making to get better results on a classification problem. The purpose of this study is to build a scholarship recipient decision support system using the Naïve Bayes Classifier method. In this study, a problem analysis was carried out using PIECES analysis and for the system development method using.The result of this research is that applying the naïve Bayes method to the scholarship recipient's decision support system can assist the school in determining the scholarship recipient more quickly and accurately. The scholarship recipient's decision support system was built using the Java programming language and MySQL database. Keyword: Decision Support Systems, Naïve Bayes Classifier, Waterfall, Blackbox Testing, PIECES