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Model-Based Testing of Access Control Requirement in Multi-tenant Application: An Extensive Life Cycle 1

3rd International Conference on Computer Science, Electrical and Electronic Engineering, ICCEE 2021

作者： Duraisamy, Gunavathi Ghani, Abdul Azim Abd Zulzalil, Hazura Abdullah, Azizol Faculty of Computer Science and Information Technology Department of Software Engineering and Information System Universiti Putra Malaysia Selangor Serdang Malaysia Faculty of Computer Science and Information Technology Department of Communication Technology and Networking Universiti Putra Malaysia Selangor Serdang Malaysia Lee Kong Chian Faculty of Engineering and Science Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Sungai Long Campus Selangor Kajang Malaysia

ISBN: (数字)9789811697814

ISBN: (纸本)9789811697807

Model-based testing is a substantial approach that is based on and involving models. It is well known for achieving test coverage and for generating and executing test cases automatically. The main and core activity of model-based testing is the modelling activity. Models are used to explain and represent the behaviour of the product. The requirement will be translated into models and those models will be used in generating the test cases automatically in model-based testing. The modelling activity clarifies test requirement and contribute to automatic generation and execution of large test suites with tool support. Model-based testing provides numerous advantages in testing;mainly on achieving appropriate test coverage and reduce manual effort and time spent since it can execute the test cases automatically. Despite all the benefit it can provide, there are some drawbacks too. However, the challenges faced can overcome by implementing proper test modelling activity and by adopting a well-versed test management activity. Thus, in this paper, we have studied the existing testing process, the taxonomy of model-based testing and model-based testing steps and proposed an extensive lifecycle for model-based testing with detail activities and steps to be carried out in order to test and validate access control requirement for multi-tenant application. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Access control

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Hybrid Power Generation Forecasting Using an Intellectual Evolutionary Energy-Preserve Rate Clustering Technique 1

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10th International Conference on Innovations in Computer Science and Engineering, ICICSE 2022

作者： webber, Julian L. Jayagopal, Vellingiri Mehbodniya, Abolfazl Sengan, Sudhakar Velayutham, Priya Rangasamy, Rajasekar Stalin David, D. Doha Kuwait Department of Software & System Engineering School of Information Technology & Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Computer Science and Engineering Paavai Engineering College Tamil Nadu Namakkal637018 India Department of Computer Science and Engineering GITAM School of Technology GITAM University Karnataka Bengaluru561203 India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Tamil Nadu Chennai600072 India

ISBN: (数字)9789811974557

ISBN: (纸本)9789811974540

The significant growth of Wind Power (WP) has significant challenges due to the inconsistency and uncertainty of Wind Power Generation (WPG) to ensure the power system. Solar energy and WP calculation are great ways to overcome the problem. Mainly voltage and frequency fluctuations and harmonics have a high impact on the power generation and distribution system;in any high case, the power quality of grid systems is reduced. This can be primarily solved with proper design, advanced rapid response control facilities and optimization of power systems. A new type of Multi-term Wind Energy (WE) with an IEEE 30 bus forecasting system based on digital weather forecasting and error modification methods is presented. From the Evolutionary Energy- Preserve Rate Clustering (EEPRC) Technique-based technique for the evaluates the probability ratio in Solar and wind error energy prediction, the error assessment model is proposed, which has the advantage of estimating from real wind farms and statistical Weather Forecast (WF) data and has proven to be a nominal method for improving the predictive accuracy of Multi-term WPG. The primary step of the Energy-Preserve Rate Clustering (EPRC) method is to calculate the uncertainty of real-time data with the predicted value. The computed values are helpful for the increases in Solar WPG production. The proposed wind forecasting system is utilized in the Coastal area-based energy generation. Also, it has been demonstrated that wind speed and WPG with its probability ratio have been evaluated with the EEPRC-based forecasting method, especially for its powerful predictive capability for the complex properties of the controller function. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Solar energy

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A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction

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Computers, Materials & Continua 2022年第10期73卷 295-310页

作者： Mohammed Gollapalli Atta-ur-Rahman Dhiaa Musleh Nehad Ibrahim Muhammad Adnan Khan Sagheer Abbas Ayesha Atta Muhammad Aftab Khan Mehwash Farooqui Tahir Iqbal Mohammed Salih Ahmed Mohammed Imran BAhmed Dakheel Almoqbil Majd Nabeel Abdullah Omer Department of Computer Information System(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Pattern Recognition and Machine Learning Lab Department of Software EngineeringGachon UniversityInchonKorea Department of Computer Science National College of Business Administration and EconomicsLahorePunjab54000Pakistan Department of Computer Science Government College University(GCU)LahorePunjab54000Pakistan Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia College of Business Administration Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Networks and Communications(NC) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various *** as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects *** congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the *** address this overwhelming problem,in this article,a cloudbased intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy *** aim of the study is to reduce the delay in the queues,the vehicles experience at different road junctions across the *** proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things(IoT)sensors across the *** due preprocessing over the cloud server,the proposed approach makes use of this data by incorporating the neuro-fuzzy ***,it possesses a high level of accuracy by means of intelligent decision making with minimum error *** results reveal the accuracy of the proposed model as 98.72%during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%,95.84%,97.56%and 98.03%,*** far as the training phase analysis is concerned,the proposed scheme exhibits 99.214% accuracy. The proposed prediction modelis a potential contribution towards smart cities environment.

关键词： Neuro-fuzzy machine learning congestion prediction AI cloud computing smart cities

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Expert Review on Usefulness of an Integrated Checklist-based Mobile Usability Evaluation Framework

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Journal of Computer Science Research 2023年第3期5卷 57-73页

作者： Hazura Zulzalil Hazwani Rahmat Abdul Azim Abd Ghani Azrina Kamaruddin Department of Software Engineering and Information System Universiti Putra MalaysiaUPM SerdangSelangor43400Malaysia Department of Information Technology Centre for Diploma StudiesUniversiti Tun Hussein Onn MalaysiaPagoh Higher Education HubPagohJohor84600Malaysia

Previous mobile usability studies are only pertinent in the context of ergonomics,physical user interface,and mobility *** addition,much of the previous mobile usability conception was built on desktop computing measurements,such as desktop and web application checklists,or scarcely addressed the mobile user ***,the studies focus mainly on interface features for desktop applications and do not reflect comprehensive mobile interface features such as navigation drawers and ***,conducting usability evaluation using conventional usability measurement would result in irrelevant *** addition,the resulting works are tailored for usability testing,which requires highly skilled evaluators and usability specialists(e.g.,usability testers and user experience designers),who are rarely integrated into a development *** lack of expertise could lead to unreliable usability *** paper presents a review from industrial experts on a comprehensive and feasible usability evaluation framework developed in our previous *** framework is dedicated to smartphone apps,which integrate evaluator skills and design ***,there is no evidence of its usefulness in ***,the usefulness of the framework measurement for evaluating apps’usability in the eyes of non-usability specialists is empirically assessed in this paper through an expert *** expert review involved eleven industrial developers and was complemented by a semi-structured *** method is replicated in comparison with a framework from another *** findings show that the formulated framework significantly outperformed the framework(p=0.0286)from other studies with large effect sizes(r=1.81)in terms of usefulness.

关键词： Usability framework Mobile usability Usability evaluation Expert review Heuristic walkthrough

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Measurement-based Verification of Quantum Markov Chains

arXiv

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arXiv 2024年

作者： Guan, Ji Feng, Yuan Turrini, Andrea Ying, Mingsheng Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Beijing100190 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Institute of Intelligent Software Guangzhou511458 China The Centre for Quantum Software and Information University of Technology SydneyNSW2007 Australia

Model-checking techniques have been extended to analyze quantum programs and communication protocols represented as quantum Markov chains, an extension of classical Markov chains. To specify qualitative temporal properties, a subspace-based quantum temporal logic is used, which is built on Birkhoff–von Neumann atomic propositions. These propositions determine whether a quantum state is within a subspace of the entire state space. In this paper, we propose the measurement-based linear-time temporal logic MLTL to check quantitative properties. MLTL builds upon classical linear-time temporal logic (LTL) but introduces quantum atomic propositions that reason about the probability distribution after measuring a quantum state. To facilitate verification, we extend the symbolic dynamics-based techniques for stochastic matrices described by Agrawal et al. (JACM 2015) to handle more general quantum linear operators (super-operators) through eigenvalue analysis. This extension enables the development of an efficient algorithm for approximately model checking a quantum Markov chain against an MLTL formula. To demonstrate the utility of our model-checking algorithm, we use it to simultaneously verify linear-time properties of both quantum and classical random walks. Through this verification, we confirm the previously established advantages discovered by Ambainis et al. (STOC 2001) of quantum walks over classical random walks and discover new phenomena unique to quantum walks. © 2024, CC BY.

关键词： Eigenvalues and eigenfunctions

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Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation

arXiv

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arXiv 2023年

作者： Su, Xingzhe Qiang, Wenwen Hu, Jie Zheng, Changwen Wu, Fengge Sun, Fuchun Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Meituan Beijing China Science & Technology on Integrated Information System Laboratory Department of Computer Science and Technology Tsinghua University Beijing China

Generative adversarial networks (GANs) have achieved remarkable progress in the natural image field. However, when applying GANs in the remote sensing (RS) image generation task, an extraordinary phenomenon is observed: the GAN model is more sensitive to the amount of training data for RS image generation than for natural image generation (Fig.1). In other words, the generation quality of RS images will change significantly with the number of training categories or samples per category. In this paper, we first analyze this phenomenon from two kinds of toy experiments and conclude that the amount of feature information contained in the GAN model decreases with reduced training data (Fig.2). Then we establish a structural causal model (SCM) of the data generation process and interpret the generated data as the counterfactuals. Based on this SCM, we theoretically prove that the quality of generated images is positively correlated with the amount of feature information. This provides insights for enriching the feature information learned by the GAN model during training. Consequently, we propose two innovative adjustment schemes, namely Uniformity Regularization (UR) and Entropy Regularization (ER), to increase the information learned by the GAN model at the distributional and sample levels, respectively. Extensive experiments on eight RS datasets and three natural datasets show the effectiveness and versatility of our methods. The source code is available at https://***/rootSue/Causal-RSGAN. Copyright © 2023, The Authors. All rights reserved.

关键词： Remote sensing

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ROS Based Multiple Service Robots Control and Communication with High Level User Instruction with Ontology 10

ROS Based Multiple Service Robots Control and Communication ...

引用

10th International Conference on information and Automation for Sustainability, ICIAfS 2021

作者： Samantha Rajapaksha, U.U. Jayawardena, Chandimal MacDonald, Bruce A. Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System and Engineering Malabe Sri Lanka The University of Auckland Department of Electrical Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9781665444293

Human Robot Interaction (HRI) is one of the biggest research field in the Robotics research world. Understanding the semantic meaning of the user instruction is very important to establish the communication between user and robot. When a user instructs to all heterogeneous service Robots with high level instructions who are working at different locations in smart house, all robots need to operate by understanding the semantic of the instruction and complete the task uniformly without considering underline software and hardware implementations. Ontology is used to extract the semantic meaning of the instructions. Each robot is assigned a specific task for specific time period for each day. User can issue commands to all robots at different time slots but the same command can be issued with different sentence by different users. We have used ontology to represents the semantic of the sentence with different commands. We have used three robots (TurtleBot, TiaGo and Husky) for our experiment in Gazebo simulator. Robot Operating system (ROS) is the middleware which is hiding more complex implementation of different functions for different robots and provide the interoperability for heterogeneous robot operations. The ROS nodes and topics can be different for different robots, therefore our implementation can solve this issue by autonomous robot registration algorithm which is working with Robot ontology. © 2021 IEEE.

关键词： Ontology

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On the Generalization and Causal Explanation in Self-Supervised Learning

arXiv

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arXiv 2024年

作者： Qiang, Wenwen Song, Zeen Gu, Ziyin Li, Jiangmeng Zheng, Changwen Sun, Fuchun Xiong, Hui The National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China The National Key Laboratory of Space Integrated Information System Department of Computer Science and Technology Tsinghua University Beijing China The Hong Kong University of Science and Technology China

Self-supervised learning (SSL) methods learn from unlabeled data and achieve high generalization performance on downstream tasks. However, they may also suffer from overfitting to their training data and lose the ability to adapt to new tasks. To investigate this phenomenon, we conduct experiments on various SSL methods and datasets and make two observations: (1) Overfitting occurs abruptly in later layers and epochs, while generalizing features are learned in early layers for all epochs;(2) Coding rate reduction can be used as an indicator to measure the degree of overfitting in SSL models. Based on these observations, we propose Undoing Memorization Mechanism (UMM), a plug-and-play method that mitigates overfitting of the pre-trained feature extractor by aligning the feature distributions of the early and the last layers to maximize the coding rate reduction of the last layer output. The learning process of UMM is a bi-level optimization process. We provide a causal analysis of UMM to explain how UMM can help the pre-trained feature extractor overcome overfitting and recover generalization. We also demonstrate that UMM significantly improves the generalization performance of SSL methods on various downstream tasks. The source code is to be released at https://***/ZeenSong/UMM. © 2024, CC BY.

关键词： Self-supervised learning

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Image De-occlusion via Event-enhanced Multi-modal Fusion Hybrid Network

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Machine Intelligence Research 2022年第4期19卷 307-318页

作者： Si-Qi Li Yue Gao Qiong-Hai Dai Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Institute for Brain and Cognitive Sciences Tsinghua UniversityBeijing 100084China Beijing Laboratory of Brain and Cognitive Intelligence Beijing Municipal Education CommissionTsinghua UniversityBeijing 100084China Key Laboratory for Information System Security School of SoftwareTsinghua UniversityBeijing 100084China Department of Automation Tsinghua UniversityBeijing 100084China

Seeing through dense occlusions and reconstructing scene images is an important but challenging *** framebased image de-occlusion methods may lead to fatal errors when facing extremely dense occlusions due to the lack of valid information available from the limited input occluded *** cameras are bio-inspired vision sensors that record the brightness changes at each pixel asynchronously with high temporal ***,synthesizing images solely from event streams is ill-posed since only the brightness changes are recorded in the event stream,and the initial brightness is *** this paper,we propose an event-enhanced multi-modal fusion hybrid network for image de-occlusion,which uses event streams to provide complete scene information and frames to provide color and texture *** event stream encoder based on the spiking neural network(SNN)is proposed to encode and denoise the event stream efficiently.A comparison loss is proposed to generate clearer *** results on a largescale event-based and frame-based image de-occlusion dataset demonstrate that our proposed method achieves state-of-the-art performance.

关键词： Event camera multi-modal fusion image de-occlusion spiking neural network(SNN) image reconstruction

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Smart Agriculture Prediction system for Vegetables Grown in Sri Lanka 12

Smart Agriculture Prediction System for Vegetables Grown in ...

引用

12th IEEE Annual information technology, Electronics and Mobile Communication Conference, IEMCON 2021

作者： Gamage, Rashmika Rajapaksa, Hasitha Sangeeth, Abhiman Hemachandra, Gimhani Wijekoon, Janaka Nawinna, Dasuni Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe 10115 Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe 10115 Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe 10115 Sri Lanka

ISBN: (纸本)9781665400664

Agriculture planning plays a dominant role in the economic growth and food security of agriculture-based countries such as Sri Lanka. Even though agriculture plays a vital role, there are still several major complications to be addressed. Some of the major complications are lack of knowledge about yield and price resulting in the farmers selecting crops based on experience. Machine learning has a great potential to solve these complications. To this end, this paper proposes a novel system comprises of a mobile application, SMS (Short Message Service), and API (Application Programming Interface) with yield prediction, price prediction, and crop optimization. Several machine learning algorithms were used for yield and price predictions while a generic algorithm was used to optimize crops. The yield was predicted considering the environmental factors while the price was predicted considering supply and demand, import and export, and seasonal effect. To select the best suitable crops to cultivate, the output of yield and price prediction have been used. Yield prediction has been implemented using elastic net, ridge, and multilinear regression. R2 of yield prediction is varied from 0.74 to 0.89 while RMSE value is between 15.69 and 35.05. Price prediction has been implemented using the algorithms of Gradient Boosting Tree, Random Forest, Facebook Prophet, and R2 is varied from 0.72 to 0.92 while RMSE value is between 26.81 and 140.72. Crop optimization has been implemented using the genetic algorithm. © 2021 IEEE.

关键词： Crops

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