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31st IEEE International Conference on Software Analysis, Evolution and Reengineering, SANER 2024

作者： Hutcheson, Richard Blanchard, Austin Lambaria, Noah Hale, Jack Kozak, David Abdelfattah, Amr S. Cerny, Tomas Baylor University Computer Science ECS Waco United States Baylor University Computer Science ECS San FranciscoCA United States Brno University of Technology Faculty of Information Technology Brno Czech Republic University of Arizona Systems and Industrial Engineering TucsonAZ United States

ISBN: (纸本)9798350330663

Microservices are the mainstream architecture when designing cloud-native systems. The performance and elastic scalability of such systems are the main attraction for many vendors. Recent advancements improving microservice initialization times are related to the ahead-of-time compilation, which produces self-contained executables, significantly reducing load times. Despite recent advancements and various benefits of cloud-native systems, the evolution of such systems might be threatened by a missing system-centered view. Such a view would guide in a better contextual understanding of individual microservices and their dependencies from the holistic system perspective and aid developers in informed decisions to mitigate ripple effects. One way literature has addressed this gap is by performing Software Architecture Reconstruction (SAR), a process essential for understanding, maintaining, and evolving software systems. This paper questions whether instruments used to produce self-contained executables for microservices can be utilized for SAR, producing system-centered views. We propose a methodology for such a process, implement a proof of concept tool, MicroGraal, for the Java Platform, and assess it through a case study involving a third-party microservice system benchmark. We uncovered a system service dependency graph and a context map, comparing the approach and obtained results with source code analysis. © 2024 IEEE.

关键词： Software architecture

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ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED CRIMINAL DETECTION USING A DEEP LEARNING APPROACH

ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Jagadeesan, S. Sujigarasharma, K. Manjula, R. Vetriselvi, T. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350305708

This study examines the accuracy and ethical implications of using convolutional neural networks (CNN) for automated crime detection. A CNN model was trained on a dataset of criminal mugshots to identify potential criminal behaviour based on facial features. This study analyzed the performance of the model and achieved a high accuracy rate in identifying criminals. However, the ethical implications of automated criminal detection are also explored, including bias, privacy and human rights violations. The findings of this study highlight the need for caution and ethical considerations when implementing automated crime detection technologies. It is important to ensure that such technologies are not used to violate the rights of individuals or perpetuate societal biases. © 2023 IEEE.

关键词： CNN Criminal detection Face detection Machine learning

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Learning of Multimodal Point Descriptors in Radar and LIDAR Point Clouds

Learning of Multimodal Point Descriptors in Radar and LIDAR ...

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International Conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Jan M. Rottter Simon Cohrs Holger Blume Bernardo Wagner Realtime Systems Group of the Faculty of Electrical Engineering and Computer Science Leibniz University of Hannover Hannover Germany Institute of Microelectronic Systems of the Faculty of Electrical Engineering and Computer Science Leibniz University of Hannover Hannover Germany

ISBN: (数字)9798350368031

ISBN: (纸本)9798350368048

Registration of point clouds is a fundamental task in robotic SLAM pipelines. Typically this task is performed only on point clouds of the same sensor or at least the same sensing modality. However, robots designed for challenging environments are often equipped with redundant sensors for the same task where some sensors are more accurate and others are more robust against disturbing environmental conditions. Being able to register the data across the modalities is an important step to more fault-tolerant localization and mapping. We therefore propose a learning framework, which describes the points in the point cloud invariant of their modality. This description is then used in a transformer-like model to find point matches for the registration process. We demonstrate our results using a scanning lidar and radar sensor on our own and publicly available datasets.

关键词： Point cloud compression Location awareness Laser radar Simultaneous localization and mapping Pipelines Robot sensing systems Transformers Sensors Registers Intelligent systems

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A conceptional structure for challenging the rebound effect of the product-service system 33

A conceptional structure for challenging the rebound effect ...

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33rd CIRP Design Conference

作者： Alfarisi, Salman Mitake, Yuya Tsutsui, Yusuke Wang, Hanfei Shimomura, Yoshiki Faculty of Systems Design Tokyo Metropolitan University Tokyo191-0065 Japan Human Augmentation Research Center National Institute of Advanced Industrial Science and Technology Chiba277-0882 Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University 719-1197 Japan

The product obsolescence rate is one of the product-service system rebound effect issues. Obsolescence can be generated by both planned obsolescence and post-production obsolescence. Planned obsolescence can increase sales and revenue artificially. However, the pattern of planned obsolescence has demonstrated that obsolescence is no longer limited to functional obsolescence but rather rapidly evolves to include quality and aesthetic aspects. Moreover, post-production consuming behaviours hasten the rate of product obsolescence, especially in the absence of ownership. At this point, rapid obsolescence contradicts the spirit of the Product-service System's (PSS) value proposition. During the stage of value co-creation, such topics must be considered. According to research on PSS and the co-creation process, the co-creation of value is inherent to PSS. Nevertheless, managing the process of value co-creation is another challenge. In addition, decision-making should also be prevented from being dominated by a high degree of centralisation. This paper presents a preliminary investigation into constructing a framework for managing the value co-creation process and decision-making to prevent the rebound effect based on conceptual review. This study identifies methods applicable to the co-creation procedure. A conceptional structure of co-creation of value and decision-making is presented. © 2023 The Authors. Published by Elsevier B.V.

关键词： Obsolescence

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Weather Satellite Tracking with RTL-SDR: NOAA 15 Image Reception and Signal Decoding 2

Weather Satellite Tracking with RTL-SDR: NOAA 15 Image Recep...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Waseem, Ahsan Kamil, Mohd Azhan Umar Bhardwaj, Abhisehek Razim, Abdul Qadeer, Mohammed Abdul Ghazal, Taher M. Aligarh Muslim University Department Of Electrical Engineering Aligarh India Aligarh Muslim University Department Of Computer Engineering Aligarh India Stony Brook University Department Of Electrical And Computer Engineering New York United States Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

This study examines the growing number of satellites, this paper explores the importance of satellite tracking and highlights NOAA-15 for information transmission and weather monitoring. It offers a low-cost configuration for receiving circularly polarized signals, using a Software Defined Radio (SDR) at 137.62 MHz and a V-dipole antenna. The directional characteristics of the V-dipole maximize capture efficiency by improving signal reception during particular satellite orbits. The flexible SDR, in particular the RTL SDR USB dongle, is used to detail signal decoding for Automatic Picture Transmission (APT), allowing for effective processing and tracking. High-quality APT photos are produced by this hardware-software integration, highlighting NOAA's crucial role in international weather forecasting. The study also emphasizes how simple and inexpensive it is to use the V-dipole antenna to receive NOAA weather satellite signals. © 2024 IEEE.

关键词： Weather forecasting

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Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2222-2232页

作者： Juanjuan Li Junqing Li Xiao Wang Rui Qin Yong Yuan Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao 266590China the School of Artificial Intelligence Anhui UniversityHefei 266114 the Qingdao Academy of Intelligent Industries Qingdao 230031China the School of Mathematics Renmin University of ChinaBeijing 100872China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 the Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive *** security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing *** this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary ***,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and ***,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic *** incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading *** temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation ***,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

关键词： Auction data trading markets multi-blockchain pricing mechanisms

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Estimating Mutual Information for Link Adaptation in Generalized Spatial Modulation systems with Neural Networks 26

Estimating Mutual Information for Link Adaptation in General...

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26th International ITG Workshop on Smart Antennas, WSA 2023 and 13th Conference on systems, Communications, and Coding, SCC 2023

作者： Bailon, Daniel Nicolas Kühn, Volker Shavgulidze, Sergo Freudenberger, Jürgen Institute for System Dynamics HTWG Konstanz University of Applied Sciences Germany Institute of Communications Engineering Faculty of Computer Science and Electrical Engineering University of Rostock Germany Faculty of Informatics and Control Systems Georgian Technical University Georgia Germany

ISBN: (纸本)9783800760510

Spatial modulation (SM) is a low-complexity multiple-input/multiple-output transmission technique that combines index modulation and quadrature amplitude modulation for wireless communications. In this work, we consider the problem of link adaption for generalized spatial modulation (GSM) systems that use multiple active transmit antennas simultaneously. Link adaption algorithms require a real-time estimation of the link quality of the time-variant communication channels, e.g., by means of estimating the mutual information. However, determining the mutual information of SM is challenging because no closed-form expressions have been found so far. Recently, multilayer feedforward neural networks were applied to compute the achievable rate of an index modulation link. However, only a small SM system with two transmit and two receive antennas was considered. In this work, we consider a similar approach but investigate larger GSM systems with multiple active antennas. We analyze the portions of mutual information related to antenna selection and the IQ modulation processes, which depend on the GSM variant and the signal constellation. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Complex networks

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Sustainable-Security Assessment Through a Multi Perspective Benchmarking Framework

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computers, Materials & Continua 2022年第6期71卷 6011-6037页

作者： Ahmed Saeed Alfakeeh Abdulmohsen Almalawi Fawaz Jaber Alsolami Yoosef B.Abushark Asif Irshad Khan Adel Aboud SBahaddad Md Mottahir Alam Alka Agrawal Rajeev Kumar Raees Ahmad Khan Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow-226025Uttar PradeshIndia Department of Computer Science and Engineering Babu Banarasi Das UniversityLucknow-226028Uttar PradeshIndia

The current cyber-attack environment has put even the most protected systems at risk as the hackers are nowmodifying technologies to exploit even the tiniest of weaknesses and infiltrate *** this situation,it’s critical to design and construct software that is both secure and *** security is the most well-defined aspect of health information software systems,it is equally significant to prioritise sustainability because any health information software system will be more effective if it provides both security and sustainability to the customers at the same *** this league,it is crucial to determine those characteristics in the systems that can help in the accurate assessment of the sustainable-security of the health information software during the development *** research work employed the Fuzzy Analytic Network Process(Fuzzy ANP)to estimate the impact of the overall sustainable-security of health information software systems and their characteristics in order to achieve a high level of ***,the study validates the efficacy of the Fuzzy ANP procedure by testing it on five different versions of a health information software system through Fuzzy Technique for Order of Preference by Similarity to Ideal Solutions(Fuzzy TOPSIS).Despite the sensitivity of the health information software systems,this study employedmultiple versions of health information software *** compared with the existing procedures for testing the sustainable-security of health information software systems,the outcomes were conclusive and significantly more *** saving time and resources,the mechanism suggested in this research work aims to establish an outline that software practitioners can follow to enhance the sustainablesecurity of health information software systems.

关键词： Sustainable-security sustainable health information software systems analytic network process fuzzy logic

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Deep-Net:Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

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computers, Materials & Continua 2023年第9期76卷 3029-3047页

作者： Muhammad Attique Khan Reham R.Mostafa Yu-Dong Zhang Jamel Baili Majed Alhaisoni Usman Tariq Junaid Ali Khan Ye Jin Kim Jaehyuk Cha Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Informatics University of LeicesterLeicesterUK Information Systems Department Faculty of Computers and Information SciencesMansoura UniversityMansouraEgypt Department of Computer Engineering College of Computer ScienceKing Khalid UniversityAbha61413Saudi Arabia Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Management Information System Department College of Business AdministrationPrince Sattam Bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea

Manual diagnosis of brain tumors usingmagnetic resonance images(MRI)is a hectic process and ***,it always requires an expert person for the ***,many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the *** paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization ***-Mobile,a pre-trained deep learning model,has been fine-tuned and twoway trained on original and enhancedMRI *** haze-convolutional neural network(haze-CNN)approach is developed and employed on the original images for contrast ***,transfer learning(TL)is utilized for training two-way fine-tuned models and extracting feature vectors from the global average pooling ***,using a multiset canonical correlation analysis(CCA)method,features of both deep learning models are fused into a single feature matrix—this technique aims to enhance the information in terms of features for better *** the information was increased,computational time also *** issue is resolved using a hybrid feature optimization algorithm that chooses the best classification *** experiments were done on two publicly available datasets—BraTs2018 and BraTs2019—and yielded accuracy rates of 94.8%and 95.7%,*** proposedmethod is comparedwith several recent studies andoutperformed *** addition,we analyze the performance of each middle step of the proposed approach and find the selection technique strengthens the proposed framework.

关键词： Brain tumor haze contrast enhancement deep learning transfer learning features optimization

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CAMRI Loss: Improving the Recall of a Specific Class without Sacrificing Accuracy

CAMRI Loss: Improving the Recall of a Specific Class without...

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作者： Nishiyama, Daiki Fukuchi, Kazuto Akimoto, Youhei Sakuma, Jun Master's Program in Computer Science Degree Programs in Systems and Information Engineering Graduate School of Science and Technology University of Tsukuba Tsukuba-shi305-8577 Japan RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Department of Computer Science Faculty of Engineering Information and Systems University of Tsukuba Tsukuba-shi305-8577 Japan

In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial to improve the recall of an important class while maintaining overall accuracy. For this problem, we found that improving the separation of important classes relative to other classes in the feature space is effective. Existing methods that give a class-sensitive penalty for cross-entropy loss do not improve the separation. Moreover, the methods designed to improve separations between all classes are unsuitable for our purpose because they do not consider the important classes. To achieve the separation, we propose a loss function that explicitly gives loss for the feature space, called class-sensitive additive angular margin (CAMRI) loss. CAMRI loss is expected to reduce the variance of an important class due to the addition of a penalty to the angle between the important class features and the corresponding weight vectors in the feature space. In addition, concentrating the penalty on only the important class hardly sacrifices separating the other classes. Experiments on CIFAR-10, GTSRB, and AwA2 showed that CAMRI loss could improve the recall of a specific class without sacrificing accuracy. In particular, compared with GTSRB's second-worst class recall when trained with cross-entropy loss, CAMRI loss improved recall by 9%. © 2023 The Institute of Electronics, Information and Communication Engineers.

关键词： Deep learning

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