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A two-level stacking model for detecting abnormal users in Wechat activities

A two-level stacking model for detecting abnormal users in W...

引用

2019 International Conference on Information Technology and computer Application, ITCA 2019

作者： Ling, Jiayuan Li, Gangmin Xi'an Jiaotong-Liverpool University Dept. of Computer Science and Software Engineering Suzhou China

ISBN: (纸本)9781728164946

Machine learning algorithms are widely employed in plenty of classification or regression problems. While in real business world, it is confronted with huge and disorder data pattern. To recognize different kinds of users on the internet accurately and fast becomes a challenge. In a Wechat online bargain activity, the staff found that some strange users are highly like robots or malicious users. Thus we tried a two-level stacking model to detect them. This design got a good result of 0.98 accuracy after the training phase and an accuracy of 0.90 in a new term of the testing set. Moreover, this model is adaptable to linear and nonlinear datasets because of its diverse stacking of first-level classifiers. Therefore, this paper indicates a potential of the stacking classification model in big data times. © 2019 IEEE.

关键词： Classification (of information)

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Two-Factor Authentication Protocol with Zero-Knowledge over an Extended Field of Elliptic Curves 6

Two-Factor Authentication Protocol with Zero-Knowledge over ...

引用

6th IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics, UkrMiCo 2023

作者： Strelkovska, Iryna Onatskiy, Oleksiy Yona, Larysa International Humanitarian University Dept. Cybersecurity Software Engineering and Computer Science Odesa Ukraine

ISBN: (纸本)9798350348484

The paper proposes a cryptographic protocol two-factor authentication with the zero-knowledge over the extended field GF(2m) on elliptic curves using biometric data and private key of the user. The implementation of a cryptographic protocol with zero-knowledge proof based on elliptic curves allows significantly reducing the size of protocol parameters and increasing the cryptographic strength (computational complexity of the breaking). The cryptographic protocol was modeled in the High-Level Protocol Specification Language, the model validation and protocol verification was performed using the Security Protocol Animator tool for Automated Validation of Internet Security Protocols and Applications. The software verification of the cryptographic protocol was performed using the software modules On the Fly Model Checker and Constraint Logic based Attack Searcher. © 2023 IEEE.

关键词： Authentication

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Risk Prediction of Opioid Dependency Using Machine Learning Based on Electronic Health Record

Risk Prediction of Opioid Dependency Using Machine Learning ...

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2023 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2023

作者： Madanian, Samaneh Dai, Sherry Natarajan, Priyadarshini Rasoulipanah, Hamidreza Auckland University of Technology Dept. of Computer Science and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350341072

Opioid abuse and dependence have emerged as a pressing global concern, posing significant challenges to public health and society. Early identification and prediction of opioid dependency represent crucial steps in mitigating its abuse impact on individuals and communities. The application of machine learning techniques to analyze medical data has opened new avenues for achieving this goal. While this field and the prediction is still in its infancy, our research explores the potential of several machine learning algorithms including LightGBM for this risk prediction. To tackle the inherent class imbalance in the MIMICIII dataset, we implemented the Synthetic Minority Oversampling Technique (SMOTE). We developed predictive models using four distinct algorithms: decision trees, random forests, support vector machines, and LightGBM. These models were meticulously evaluated to assess their performance. Ultimately, our findings revealed that the LightGBM model outperformed the other algorithms, demonstrating superior accuracy and achieving a higher Area Under the Curve value. This outcome underscores the potential of LightGBM as a valuable algorithm in the early prediction of the risk of opioid dependence, thereby offering substantial benefits to both patients and society at large. © 2023 IEEE.

关键词： Risk assessment

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Directional Motion-based Interfaces for Virtual and Augmented Reality Head-mounted Displays

Directional Motion-based Interfaces for Virtual and Augmente...

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2018 International computers, Signals and Systems Conference, ICOMSSC 2018

作者： Xu, Wenge Liang, Haining Yue, Yong Xi'an Jiaotong Liverpool University Dept. of Computer Science and Software Engineering Suzhou China

ISBN: (纸本)9781538667514

This research, conducts an experiment to investigate whether there is a difference in user performance and preference for two types of head-mounted displays (HMDs) when users need to perform directional motion movements such as moving one foot forward and backward (or leftward or rightward). The two types of HMDs we have considered are virtual reality (VR) and augmented reality (AR), which represent the two most commonly marketed HMDs. The AR device chosen for this research is the Meta 2 while the chosen VR device is the Oculus RIFT CV1. The results of our experiment show that there is a close significant difference on task completion time between AR and VR condition when users perform motion direction tasks. Also, no significant effect has been found on the accuracy of making these motion movements between the AR and VR conditions. In terms of user preference, the results show that there is no significant effect on workload, motion sickness, immersion, and user experience. These results suggest that both AR and VR HMDs are suitable for interfaces that can rely on body motions like tapping on the floor using one foot in any of the directions around the user. © 2018 IEEE.

关键词： Augmented reality

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Comparative study of feature selection for white blood cell differential counts in low resolution images 6

引用

6th IAPR TC3 International Workshop on Artificial Neural Networks for Pattern Recognition, ANNPR 2014

作者： Habibzadeh, Mehdi Krzyżak, Adam Fevens, Thomas Dept. Of Computer Science and Software Engineering Concordia University MontréalQC Canada

ISBN: (纸本)9783319116556

Features that are widely used in digital image analysis and pattern recognition tasks are from three main categories: shape, intensity, and texture invariant features. For computer-aided diagnosis in medical imaging for many specific types of medical problem, the most effective choice of a subset of these features through feature selection is still an open problem. In this work, we consider the problem of white blood cell (leukocyte) recognition into their five primary types: Neutrophils, Lymphocytes, Eosinophils, Monocytes and Basophils using a Support Vector Machine classifier. For features, we use four main intensity histogram calculations, set of 11 invariant moments, the relative area, co-occurrence and run-length matrices, dual tree complex wavelet transform, Haralick and Tamura features. Global sensitivity analysis using Sobol’s RS-HDMR which can deal with independent and dependent input variables is used to assess dominate discriminatory power and the reliability of feature models in presence of high dimensional input feature data to build an efficient feature selection. Both the numerical and empirical results of experiments are compared with forward sequential feature selection. Finally, the results obtained from the preliminary analysis of white blood cell classification are presented in confusion matrices and interpreted using Cohen’s kappa (κ) with the classification framework being validated with experiments conducted on poor quality white blood cell images. © Springer International Publishing Switzerland 2014.

关键词： Medical imaging

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Dual clustering for categorization of action sequences

Dual clustering for categorization of action sequences

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作者： Cheng, Joanna Wang, Liang Leckie, Christopher Dept. Computer Science and Software Engineering University of Melbourne Parkville VIC 3010 Australia

ISBN: (纸本)9781424421756

This paper proposes a novel algorithm for categorization of action video sequences using unsupervised dual clustering. Given a video database, we extract motion information of actions and perform nonlinear dimensionality reduction for addressing both the high dimensionality of silhouette features and non-linearity of articulated human actions. A k-means clustering is first performed on frame-wise features in the embedding space to convert each video in the database to a sequence of labels, each of which corresponds to one of k "key" feature frames. The dissimilarity between any two label sequences is then measured using edit distance. The resulting pairwise dissimilarity matrix is finally input to a spectral clustering algorithm to obtain the category labels of each action video. Experimental results on two recent data sets demonstrate the effectiveness and efficiency of the proposed algorithm. © 2008 IEEE.

关键词： Clustering algorithms

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Android malware detection using feature selections and random forest 4

Android malware detection using feature selections and rando...

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4th International Conference on software Security and Assurance, ICSSA 2018

作者： Eom, Taehoon Kim, Heesu An, SeongMo Park, Jong Sou Kim, Dong Seong Dept. of Computer Engineering Korea Aerospace University Seoul Korea Republic of Dept. of Computer Science and Software Engineering University of Queensland Australia

ISBN: (纸本)9781538692103

Malicious software (Malware) applications in Android ecosystem is one of the critical issues. Manual detection of malware is not cost-effective and cannot keep up with the fast evolution of malware development in Android. A machine learning based malware detection has attempted to automate the detection of malware in Android. In this paper, we present new Android malware detection methods. The main idea of our proposed approach is to use three different feature selection methods before malware detection model using a machine learning algorithm is constructed. We used both Malware Genome Project dataset and our own crawled dataset to show the effectiveness of the proposed methods. © 2018 IEEE.

关键词： Feature Selection

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Expressive power of logic frameworks with certainty constraints

Expressive power of logic frameworks with certainty constrai...

引用

Recent Advances in Artifical Intelligence - Eighteenth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2005

作者： Shiri, Nematollaah Concordia University Dept. of Computer Science and Software Engineering Montreal Que. Canada

ISBN: (纸本)1577352343

Uncertainty reasoning has been a challenging issue in AI and database research. During the last two decades, numerous frameworks have been proposed by extending the standard logic programming and deductive database systems. These frameworks vary in different ways including the way in which uncertainties are associated with the facts and rules in programs. On the basis of this, we have classified the approaches of these frameworks into "annotation based" (AB) and "implication based" (IB). In the AB approach, uncertainties are associated with the atoms whereas in the IB approach they are associated with implications. Except for some particular cases, the exact relationship between the two approaches has not been studied previously, in that, the two are treated as somewhat orthogonal and unrelated. In this paper, we investigate this issue and introduce the notion of certainty constraint. The purposes are two fold. First, this notion supports the operations of selection and join by certainty, which are often useful in query formulation and processing in the context of uncertainty. Second and more importantly, this notion "relates" the expressive power of the two approaches. To be precise, we propose a transformation technique which establishes that the AB and IB approaches are equally expressive when the rule bodies are extended with certainty constraints. A consequence of this result is that, using the transformation technique, we can adopt and use successful developments in either approach. Copyright © 2005, American Association for Artificial Intelligence (***). All rights reserved.

关键词： Formal logic

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Comparative evaluation of change propagation approaches towards resilient software evolution

Comparative evaluation of change propagation approaches towa...

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3rd International Conference on software engineering Advances, ICSEA 2008

作者： Ibrahim, Noraini Kadir, Wan M. Nasir Wan Deris, Safaai Software Engineering Dept. Faculty of Computer Science and Information System Universiti Teknologi Malaysia

ISBN: (纸本)9780769533728

Producing software that is adaptable to the rapid environmental changes and the dynamic nature of the business life-cycle is extensively becoming a topical issue in the software evolution. In this context, change propagation (CP) process is one of the critical parts in the software change management. Traditional strategies have projected more complex ways, resulting to substantial failures and risks. This paper presents an investigation and highlights on the desired criteria to provide better means to simplify the complicated CP tasks. The evaluation results may be used as a foundation in improving CP approaches that provide significant challenges in software evolution. ©2008 IEEE.

关键词： Life cycle

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A software Framework Based on JUnit for Automated software Testing in computer science Courses 54

A Software Framework Based on JUnit for Automated Software T...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Zmuda, Michael A. Miami University Dept. of Computer Science and Software Engineering OxfordOH United States

ISBN: (纸本)9798350351507

This innovative practice full paper describes a new software framework based on JU nit to test student work. Automated testing is an important capability when teaching software development at the college level. Ideally, a testing system will allow the instructor to efficiently create a thorough set of tests. Also, the software should facilitate grading tasks and produce informative reports that can be distributed to the students in a timely fashion. For Java development, the well-known JUnit framework enables a test suite to be applied to a student's submission. The mutools library presented here extends the JUnit framework in novel ways to accelerate the instructor's task of creating test suites. This new framework allows the instructor to augment tests with directives to control scoring and reporting. The four main capabilities of the software include: 1) An assert statement that does not terminate the test when it has failed. Instead, statistics are maintained regarding the success or failure of each assert statement. 2) Tests that can be configured to award partial credit. This can be useful in situations where the instructor deems it appropriate to award students some credit even in the presence of incorrect asserts. 3) Tests that can be grouped into categories that match a particular rubric item. Java annotations are placed on the test suite to define these categories. For example, @TestCategory(name= 'remove', points=10.0) specifies that 10 points will be awarded for successful implementation of all tests related to removing an item from the collection class. 4) Testing reports that contain varying levels of information. With minimal details, the testing report shows each testing category with the following information: assert statistics, whether the test timed out or had abnormal termination. This software has been used for many semesters and has been found to increase the speed at which the instructor can develop test suites for grading. The framework is av

关键词： software testing

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