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22nd IEEE/ACIS International Conference on Software engineering Research, Management and Applications, SERA 2024

作者： Leelaprute, Pattara Kase, Yuki Amasaki, Sousuke Aman, Hirohisa Yokogawa, Tomoyuki Kasetsart University Department of Computer Engineering Faculty of Engineering Bangkok Thailand Okayama Prefectural University Department of Systems Engineering Soja Japan Center for Information Technology Ehime University Matsuyama Japan

ISBN: (纸本)9798350391343

CONTEXT: Web applications are exposed to malicious accesses through the Internet. SQL injection (SQLi) attacks are still a typical threat to web application providers. Although recent studies proposed deep learning-based SQLi attack detection models with high performance, those studies were not evaluated under the same conditions. Crucial aspects other than the predictive performance were also overlooked. OBJECTIVE: To evaluate SQLi attack detection models from multi-aspects related to its operation. METHOD: Three aspects, namely, predictive performance, detection speed, and operation costs, were applied to deep learning-based SQLi attack detection models. RESULTS: No DL-based model beaten a conventional machine learning-based model, Random Forests. CONCLUSION: Researchers must evaluate DL-based SQLi attack detection models with conventional ones with hyper-parameter tuning. © 2024 IEEE.

关键词： Contrastive Learning

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Constructed TiO_(2)/WO_(3)heterojunction with strengthened nano-trees structure for highly stable electrochromic energy storage device

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Journal of Advanced Ceramics 2023年第3期12卷 634-648页

作者： Lili Zhao Zhuoan Cai Xiaoyang Wang Wenbo Liao Simin Huang Lingyun Ye Jilie Fang Chunxing Wu Hao Qiu Lei Miao School of Chemical Engineering and Energy Technology Dongguan University of TechnologyDongguan 523808China Guangxi Key Laboratory of Information Materials Engineering Research Center of Electronic Information Materials and DevicesMinistry of EducationSchool of Materials Science and EngineeringGuilin University of Electronic TechnologyGuilin 541004China Guangdong Provincial Key Laboratory of Distributed Energy Systems Dongguan 523808China

Tungsten trioxide(WO_(3))has been widely regarded as a prospective bifunctional material due to its electrochromic and pseudocapacitive properties,while still facing the dilemma of inadequate cycle stability and trapping-induced ***,inspired by the trees-strengthening approach,a unique titanium dioxide(TiO_(2))nanorod arrays strengthened WO_(3)nano-trees(TWNTs)heterojunction was rationally designed and *** sharp contrast to the transmittance modulation(ΔT)attenuation of primary WO_(3)nano-trees during cycling,the TWNTs film showed not only excellent electrochromic performance but also fascinating cycle stability(77.35%retention of the initialΔT after 10,000 cycles).Besides,the trapping-induced degradation could be easily rejuvenated by a potentiostatic de-trapping *** electrochromic energy storage device(EESD)was further assembled based on the TWNTs film to deliver excellentΔT(up to 79.5%at 633 nm),fast switching speed(tc/tb=1.9 s/14.8 s),extremely high coloration efficiency value(443.4 cm^(2)·C^(−1)),and long-term cycle stability(over 10,000 charge/discharge cycles).This innovative study provided in-depth insights into the electrochromism nature and a significant step in the realization of stable electrochromic-energy storage application,paving the way for multifunctional smart windows as well as next-generation optoelectronic devices.

关键词： electrochromic(EC) WO_(3)nano-trees cycle stability TiO_(2)nanorod arrays(TiO_(2)NRAs) energy storage

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Cooperative Lane Changing in Mixed Traffic Can Be Robust to Human Driver Behavior

Cooperative Lane Changing in Mixed Traffic Can Be Robust to ...

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IEEE Conference on Decision and Control

作者： Anni Li Andres S. Chavez Armijos Christos G. Cassandras Division of Systems Engineering and Center for Information and Systems Engineering Boston University Brookline MA

We derive time and energy-optimal control policies for a Connected Autonomous Vehicle (CAV) to complete lane change maneuvers in mixed traffic. The interaction between CAVs and Human-Driven Vehicles (HDVs) requires the best possible response from a CAV to actions by its neighboring HDVs. This interaction is formulated using a bilevel optimization setting with an appropriate behavioral model for an HDV. An iterated best response (IBR) method is then used to determine a Nash equilibrium. However, we also show that CAV cooperation can eliminate or greatly reduce the interaction between CAVs and HDVs. We derive a simple threshold-based criterion to select an optimal policy for the lane-changing CAV to merge ahead of a cooperating CAV in the target lane. In this case, the trajectory of the lane-changing CAV is independent of HDV behavior. Simulation results are included to demonstrate the effectiveness of our CAV controllers in terms of minimizing cost and disruption to traffic flow while guaranteeing safety when uncontrollable HDVs are present.

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Maximizing Safety and Efficiency for Cooperative Lane-Changing: A Minimally Disruptive Approach

Maximizing Safety and Efficiency for Cooperative Lane-Changi...

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International Conference on Intelligent Transportation

作者： Andres S. Chavez Armijos Anni Li Christos G. Cassandras Division of Systems Engineering and the Center for Information and Systems Engineering Boston University Brookline MA

This paper addresses cooperative lane-changing maneuvers in mixed traffic, aiming to minimize traffic flow disruptions while accounting for uncooperative vehicles. The proposed approach adopts controllers combining Optimal control with Control Barrier Functions (OCBF controllers) which guarantee spatio-temporal constraints through the use of fixed-time convergence. Additionally, we introduce robustness to disturbances by deriving a method for handling worst-case disturbances using the dual of a linear programming problem. We present a near-optimal solution that ensures safety, optimality, and robustness to changing behavior of uncooperative vehicles. Simulations demonstrate the effectiveness of the proposed approach in enhancing efficiency and safety.

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Scalable Adaptive Traffic Light Control Over a Traffic Network Including Transit Delays

Scalable Adaptive Traffic Light Control Over a Traffic Netwo...

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International Conference on Intelligent Transportation

作者： Yingqing Chen Christos G. Cassandras Division of Systems Engineering and Center for Information and Systems Engineering Boston University Brookline MA

We study the Traffic Light Control (TLC) problem for a traffic network with multiple intersections in an artery, including the effect of transit delays for vehicles moving from one intersection to the next. The goal is to minimize the overall mean waiting time and improve the “green wave” properties in such systems. Using a stochastic hybrid system model with parametric traffic light controllers, we use Infinitesimal Perturbation Analysis (IPA) to derive a data-driven cost gradient estimator with respect to these parameters. We then iteratively adjust them through an online gradient-based algorithm. We show that the event-driven nature of the IPA estimators driving the controllers leads to scalable computationally efficient controllers as the dimensionality of the traffic network increases.

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A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

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Computers, Materials & Continua 2024年第8期80卷 1867-1888页

作者： Qussai M.Yaseen Monther Aldwairi Ahmad Manasrah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjman346United Arab Emirates Department of Computer Information Systems Faculty of Computer and Information TechnologyJordan University of Science and TechnologyIrbid22110Jordan College of Technological Innovation Zayed UniversityAbu Dhabi144534United Arab Emirates Faculty of Information Technology and Computer Science Yarmouk UniversityIrbid21163Jordan Computer Information Systems Higher Colleges of TechnologySharjah341214United Arab Emirates

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their ***,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN *** paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering *** proposed version employs a network-based reputation system to select the best and most secure path to a *** achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation *** minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation ***,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast *** proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other *** the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing *** paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different ***,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

关键词： AODV blackhole malfunction nodes pathrater reputation system wireless sensor networks(WSNs) watchdogs

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TRU GENERATION SUPPRESSION BY HALEU FUEL WITH FIXED DISCHARGE BURNUP USING BURNABLE POISON 30

TRU GENERATION SUPPRESSION BY HALEU FUEL WITH FIXED DISCHARG...

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30th International Conference on Nuclear engineering, ICONE 2023

作者： Hiraiwa, Kouji Yoshioka, Kenichi Kimura, Rei Takeda, Satoshi Wada, Satoshi Kitada, Takanori Sugita, Tsukasa Isogo Engineering Center Toshiba Energy Systems & Solutions Corporation Yokohama Japan Nuclear Engineering Laboratory Toshiba Energy Systems & Solutions Corporation Kawasaki Japan Energy Systems Research and Development Center Toshiba Energy Systems & Solutions Corporation Kawasaki Japan Graduate School of Engineering Division of Sustainable Energy and Environmental Engineering Quantum and Energy Engineering Area Osaka University Osaka Japan

ISBN: (纸本)9784888982566

Effects of burnable poison (BP) addition on radiotoxicity by TRU were investigated for FORSETI fuel, which is assumed to be loaded into a BWR plant. Since the purpose of this study was to confirm the relative change in radiotoxicity due to TRU, the weight ratio of TRU was used as an index of the relative magnitude of radiotoxicity. The weight fraction of TRU nuclides with 235U enrichment from 3.8 wt% to 20 wt% was examined with and without BP. The weight fraction of TRU nuclides in the spent fuel decreased by about 60% for 238Pu, 240Pu, and 241Pu. The results of these nuclides are almost the same between with and without BP. On the other hand, 239Pu, which is not dominant in the toxicity of TRU, is slightly increased by the addition of BP. The relative difference in TRU weight fraction between with-BP and without-BP by increase of 235U enrichment does not exceed 20wt% except for *** is desirable to consider the error in the amount of TRU generation when evaluating without BP for analytical simplicity. © 2023 by JSME.

关键词： Isotopes

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Optimizing beyond boundaries: empowering the salp swarm algorithm for global optimization and defective software module classification

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Neural Computing and Applications 2024年第30期36卷 18727-18759页

作者： Kassaymeh, Sofian Al-Betar, Mohammed Azmi Rjoubd, Gaith Fraihat, Salam Abdullah, Salwani Almasri, Ammar Software Engineering Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan College of Engineering and Information Technology Ajman University Ajman United Arab Emirates Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Concordia Institute for Information Systems Engineering Concordia University Montreal Canada Artificial Intelligence Department Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Data Mining and Optimization Research Group Center for Artificial Intelligence Technology Universiti Kebangsaan Malaysia Bangi Malaysia Management Information Systems Department Al-Balqa Applied University Amman Jordan

This work presents a new version of the salp swarm optimizer (SSA), called "mSSA," that uses complex mathematical expressions to dynamically manipulate the crucial control parameter (c1) during optimization. These expressions are carefully designed to modulate the shift in search strategy from exploratory to exploitative, improving the flexibility and speed of convergence of the algorithm. To evaluate the performance of the developed mSSA variants, a thorough examination is carried out on twenty-three benchmark test functions alongside their application to the complex task of software module classification. The process of classifying defective software modules involves developing a multilayer perceptron (MLP) classifier that is suited to the particular complexity and heterogeneity of the task. Selecting the best optimizer is made easier by systematically evaluating the different mSSA versions as MLP classifier trainers. Based on metrics like classification accuracy, convergence speed, and avoidance of local minima, a comparative analysis in opposition to six previously published metaheuristic optimizers shows that mSSA3, when combined with the developed MLP classifier, outperforms both other mSSA variations and state-of-the-art metaheuristic optimizers in terms of overall performance. The excellent classification accuracy, swift convergence, and ability to avoid local minima of mSSA3 highlight its superiority and establish it as a cutting-edge method in the application of metaheuristic algorithms. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Global optimization

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From Observing to Understanding: Empirical Insights on the Organizational Foundations of Security Chaos engineering 44

From Observing to Understanding: Empirical Insights on the O...

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44th International Conference on information systems: Rising like a Phoenix: Emerging from the Pandemic and Reshaping Human Endeavors with Digital Technologies, ICIS 2023

作者： Strobel, Jacqueline Weiß, Florian Bitzer, Michael FIM Research Center for Information Management University of Bayreuth Bayreuth Germany FIM Research Center for Information Management Branch Business & Information Systems Engineering of the Fraunhofer FIT Bayreuth Germany FIM Research Center for Information Management Augsburg University of Applied Sciences Branch Business & Information Systems Engineering of the Fraunhofer FIT Augsburg Germany

ISBN: (纸本)9781713893622

Cloud computing has become an integral part of modern corporate IT infrastructures. However, conventional IT-security measures cannot cope with its specific technical needs resulting from complexity, virtualization, or multi-tenancy as well as the need for holistic security approaches incorporating both technological and organizational perspectives on security. Security Chaos engineering (SCE) constitutes a promising approach to overcome these shortcomings. Unfortunately, existing literature focuses on technical aspects of SCE and neglects the organizational perspective, i.e., which organizational success factors need to be addressed for a successful implementation. To close this gap, we conducted an interview study following the approach of Gioia et al. (2013) and identified seven success factors related to goals, social structure, participants, and technology within a company following Scott (1981). Furthermore, we found that these organizational success factors are not only the basis for the introduction of SCE but represent common requirements for holistic security approaches in general, too. © 2023 International Conference on information systems, ICIS 2023: "Rising like a Phoenix: Emerging from the Pandemic and Reshaping Hu. All Rights Reserved.

关键词： information systems

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Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

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Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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