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6th International Conference on Recent Innovations in computing, ICRIC 2023

作者： Mahbub, Saifullah Easin Arafat, Md. Rahman, Chowdhury Rafeed Ferdows, Zannatul Hasan, Masum Department of Computer Science and Engineering United International University United City Madani Avenue Dhaka1212 Bangladesh Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Pázmány Péter Str. 1/A Budapest1117 Hungary Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka1000 Bangladesh School of Computing National University of Singapore Singapore Singapore Department of Computer Science University of Rochester RochesterNY United States

ISBN: (纸本)9789819734412

Code review is considered a key process in the software industry for minimizing bugs and improving code quality. Inspection of review process effectiveness and continuous improvement can boost development productivity. Such inspection is a time-consuming and human-bias-prone task. We propose a semi-supervised learning-based system ReviewRanker which is aimed at assigning each code review a confidence score which is expected to resonate with the quality of the review. Our proposed method is trained based on simple and well-defined labels provided by developers. The labeling task requires little to no effort from the developers and has an indirect relation to the end goal (assignment of review confidence score). ReviewRanker is expected to improve industry-wide code review quality inspection by reducing human bias and the effort required for such a task. The system has the potential of minimizing the back-and-forth cycle existing in the development and review process. Usable code and dataset for this research can be found at: https://***/saifarnab/code_review. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Self-supervised learning

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Design Thinking Method: Qurban Animal Application in Facing Eid Al-Adha in Indonesia 6

Design Thinking Method: Qurban Animal Application in Facing ...

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6th International Conference on Information technology, InCIT 2022

作者： Tarigan, Emiya Fefayosa Br Yasirandi, Rahmat Almarshad, Fahdah Insan, Isa Mulia Suwastika, Novian Anggis Telkom University Bandung School of Computing Indonesia Techology King Mongkut's Institute of Technology Ladkrabang Telkom University Bangkok Faculty of Information Thailand Prince Sattam Bin Abdulaziz University Al-Kharj Departement of Information System Saudi Arabia Institut Teknologi Sepuluh Nopember Surabaya Departement of Information System Indonesia Universiti Teknologi Malaysia Kuala Lumpur Razak Faculty of Technology and Informatics Malaysia

ISBN: (纸本)9781665489126

Technological innovation in Indonesia has brought many advantages. Religious activities are no exception. Islam, as Indonesia's majority religion, is one of those affected by the positive impact. Many technological innovations have helped the activities of Muslims in Indonesia. This includes the Eid Al-Adha event as well as the case study proposed in this study. By using the Design Thinking Approach, this study succeeded in proposing an application related to sacrificial animals. The application prototype is thriving with a proper acceptance level. This study conducted System Usability Scale Testing for 46 participants at the test stage, with SUS's score of 78.07, means the proposed prototype is included in the 'Good' Grade and 'Acceptable' category. This means the fulfilment of the needs and functionality of prospective users has been successfully provided. © 2022 IEEE.

关键词： Animals

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Doubly majorized algorithm for sparsity-inducing optimization problems with regularizer-compatible constraints

arXiv

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arXiv 2022年

作者： Liu, Tianxiang Pong, Ting Kei Takeda, Akiko Department of Mathematical and Computing Science School of Computing Tokyo Institute of Technology Tokyo Japan Department of Applied Mathematics Hong Kong Polytechnic University Hong Kong Department of Creative Informatics Graduate School of Information Science and Technology University of Tokyo Tokyo Japan RIKEN Center for Advanced Intelligence Project Tokyo Japan

We consider a class of sparsity-inducing optimization problems whose constraint set is regularizer-compatible, in the sense that, the constraint set becomes easy-to-project-onto after a coordinate transformation induced by the sparsity-inducing regularizer. Our model is general enough to cover, as special cases, the ordered LASSO model in [21] and its variants with some commonly used nonconvex sparsity-inducing regularizers. The presence of both the sparsity-inducing regularizer and the constraint set poses challenges on the design of efficient algorithms. In this paper, by exploiting absolute-value symmetry and other properties in the sparsity-inducing regularizer, we propose a new algorithm, called the Doubly Majorized Algorithm (DMA), for this class of problems. The DMA makes use of projections onto the constraint set after the coordinate transformation in each iteration, and hence can be performed efficiently. Without invoking any commonly used constraint qualification conditions such as those based on horizon subdifferentials, we show that any accumulation point of the sequence generated by DMA is a so-called ψopt-stationary point, a new notion of stationarity we define as inspired by the notion of L-stationarity in [2,3]. We also show that any global minimizer of our model has to be a ψopt-stationary point, again without imposing any constraint qualification conditions. Finally, we illustrate numerically the performance of DMA on solving variants of ordered LASSO with nonconvex regularizers. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

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KartalOl: a new deep neural network framework based on transfer learning for iris segmentation and localization task—new dataset for iris segmentation

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Iran Journal of Computer Science 2023年第4期6卷 307-319页

作者： Khiarak, Jalil Nourmohammadi Oskouei, Amin Golzari Nasab, Samaneh Salehi Jaryani, Farhang Moafinejad, Seyed Naeim Pourmohamad, Rana Amini, Yasin Noshad, Morteza Institute of Control and Computation Engineering Warsaw University of Technology Ul. Nowowiejska 15/19 Warsaw 00-665 Poland Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran School of Engineering Lorestan University Aleshtar Campus Aleshtar Iran Faculty of Engineering Arak University Arak Iran Department of Physics Shahid Beheshti University Tehran Iran School of Computing Informatics and Decision Systems Engineering Arizona State University AZ United States Faculty of Electrical and Computer Engineering University of Kharazmi Tehran Iran Stanford University Stanford United States

Iris segmentation and localization in unconstrained environments are challenging due to long distances, illumination variations, limited user cooperation, and moving subjects. Some existing methods in the literature have somehow mitigated some of the above-mentioned issues. In this paper, motivated by these weaknesses, we propose a framework that employs a deep neural network-based approach to iris segmentation and localization. The proposed framework is based on a U-Net architecture initialized with a pre-trained MobileNetV2 model. In addition, to better study the detectors in iris recognition scenarios, we have collected 1000 images. The provided dataset (KartalOl) is made publicly available for the research community. In the proposed framework, to have better generalization, we fine-tuned the MobileNetV2 model on the provided data for NIR-ISL 2021 from the CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa and our dataset. Likewise, data augmentation techniques are applied on images. We chose the binarization threshold for the binary masks by iterating over the images in the provided dataset. The proposed framework is trained and tested in CASIA-Iris-Asia, CASIA-Iris-M1, and CASIA-Iris-Africa, along with the KartalOl dataset. The experimental results highlight that our method surpasses state-of-the-art methods on mobile-based benchmarks. The implementation source code of KartalOl is made publicly available at https://***/Jalilnkh/KartalOl-NIR-ISL2021031301. © The Author(s) 2023.

关键词： Iris segmentation Localization MobileNetV2 model Unconstrained environment

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Situating Network Infrastructure with People, Practices, and Beyond: A Community Building Workshop 25

Situating Network Infrastructure with People, Practices, and...

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25th ACM Conference on Computer-Supported Cooperative Work and Social computing, CSCW 2022

作者： Jang, Esther Han Beol Bidwell, Nicola Liu, Jen Sengers, Phoebe Bagalkot, Naveen Verdezoto, Nervo Densmore, Melissa Vigil-Hayes, Morgan Hasan, Shaddi Paul G. Allen School for Computer Science and Engineering SeattleWA United States International University of Management Windhoek Namibia Cornell University Information Science IthacaNY United States Cornell University Information Science and Science & Technology Studies IthacaNY United States Srishti Manipal Institute of Art Design and Technology Karnataka Bengaluru India Cardiff University School of Computer Science and Informatics Cardiff United Kingdom University of Cape Town Cape Town South Africa Northern Arizona University School of Informatics Computing and Cyber Systems FlagstaffAZ United States Virginia Tech Department of Computer Science BlacksburgVA United States

ISBN: (纸本)9781450391900

Our world is now connected and even entangled in unprecedented ways through networked technologies. Yet pockets of unequal connectivity persist, and technical infrastructures for connectivity remain difficult to design and build even for experts. In this workshop we aim to bring together a global community of multi- and inter-disciplinary researchers and implementers working on infrastructure development and connectivity to explore the existing design challenges and opportunities for bringing technical dimensions of networked infrastructures in conversation with human-computer interaction (HCI) and the social science of infrastructure. We will share, assess and define research problems and resources for rethinking networked infrastructures from human-, community-, and society-centered perspectives, understanding them to be embedded with human values and biases. We particularly intend our collaborative work to support real-world connectivity initiatives, which have grown in critical importance over the pandemic years - especially projects in support of Global South communities. Concrete deliverables from the workshop will include: (1) an initial shared bibliography to help formalize the state of knowledge in our area, (2) an agenda of shared goals, challenges, and intentions in our field, (3) a compilation of resources to support future work, and (4) social and organizing infrastructures for continued communication and academic collaboration. © 2022 Owner/Author.

关键词： Human computer interaction

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5G Indoor Positioning Performance Evaluation Based on Semi-Deterministic Channel Model

5G Indoor Positioning Performance Evaluation Based on Semi-D...

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International Conference on Information, Communication and Networks (ICICN)

作者： Chenxin Zhang Xueting Xu Ao Peng The National Joint Engineering Center of Navigation and Location-Based-Service Technology (JECNALT) School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Suzhou China School of Informatics Xiamen University Xiamen China

ISBN: (纸本)9781665490832

The 5th generation mobile communication system is considered a promising technical solution for the universal indoor positioning problem due to its high bandwidth, dense base stations, and large antenna scale. Indoor scenes are characterised by a deterministic propagation environment. The positioning performance evaluation of statistical channel models in specific indoor scenes is subject to non-negligible deviations, and deterministic channel modelling therefore requires appropriately detailed propagation environment information. To this end, this paper improves a semi-deterministic channel modelling method. The method is based upon the statistical characteristics of the scene data and uses a ray-tracing technique in accordance with key propagation environment information. Direct radiation, reflection, and scattering mechanisms of multipath signals are considered as part of the model. Additionally, it is capable of mining the inherent propagation characteristics of wireless signals, and evaluating the positioning performance in specific indoor scenarios. The channel model allows effortless access to a location fingerprint database based on 5G wireless signals in specific indoor scenarios. Through controlled experiments, we evaluated the impact of various factors on the positioning performance of the fingerprint database. The experimental results provide us with a direction to improve positioning performance.

关键词： Wireless communication Performance evaluation Databases 5G mobile communication Scattering Fingerprint recognition Ray tracing

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NeRF-Editing: Geometry Editing of Neural Radiance Fields

arXiv

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arXiv 2022年

作者： Yuan, Yu-Jie Sun, Yang-Tian Lai, Yu-Kun Ma, Yuewen Jia, Rongfei Gao, Lin Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences China School of Computer and Control Engineering University of Chinese Academy of Sciences China School of Computer Science & Informatics Cardiff University United Kingdom Alibaba Group China

Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great potential in novel view synthesis of a scene. However, current NeRF-based methods cannot enable users to perform user-controlled shape deformation in the scene. While existing works have proposed some approaches to modify the radiance field according to the user's constraints, the modification is limited to color editing or object translation and rotation. In this paper, we propose a method that allows users to perform controllable shape deformation on the implicit representation of the scene, and synthesizes the novel view images of the edited scene without re-training the network. Specifically, we establish a correspondence between the extracted explicit mesh representation and the implicit neural representation of the target scene. Users can first utilize well-developed mesh-based deformation methods to deform the mesh representation of the scene. Our method then utilizes user edits from the mesh representation to bend the camera rays by introducing a tetrahedra mesh as a proxy, obtaining the rendering results of the edited scene. Extensive experiments demonstrate that our framework can achieve ideal editing results not only on synthetic data, but also on real scenes captured by users. Copyright © 2022, The Authors. All rights reserved.

关键词： Mesh generation

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A Toolbox of Feminist Wonder: Theories and methods that can make a difference 26

A Toolbox of Feminist Wonder: Theories and methods that can ...

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26th ACM Conference on Computer-Supported Cooperative Work and Social computing, CSCW 2023

作者： Hansson, Karin Bardzell, Shaowen Bhandari, Aparajita Boulicault, Marion Doyle, Dylan Thomas Erete, Sheena Cerratto Pargman, Teresa Lazem, Shaimaa Muller, Michael Normark, Maria Petterson, Adrian Rosén, Anton Poikolainen Taylor, Alex S. Thomas, Jakita O. Watson, Julia Södertörn University The School of Natural Sciences Technology and Environmental Studies Sweden Georgia Institute of Technology The School of Interactive Computing United States Cornell University Department of Communication United States University of Edinburgh Department of Philosophy United States University of Colorado Boulder Department of Information Science United States University of Maryland College of Information United States Stockholm University The Department of Computer and Systems Sciences Sweden City for Scientific Research and Technological Applications The Informatics Research Institute Egypt Ibm Research Human Centered Ai United States Uppsala University Department of Informatics and Media Sweden University of Toronto Faculty of Information Canada Aalto University The School of Arts Design and Architecture Finland City University of London The Centre for Human Computer Interaction Design United Kingdom Auburn University Department of Computer Science and Software Engineering United States University of Toronto Department of Computer Science Canada

ISBN: (纸本)9798400701290

This one-day hybrid workshop builds on previous feminist CSCW workshops to explore feminist theoretical and methodological approaches that have provided us with useful tools to see things differently and make space for change. Since its inception over a decade ago, feminist HCI has progressed from the margins to mainstream HCI, with numerous references in the literature. Feminist HCI has also evolved to incorporate other critical HCI practices such as Queer HCI, participatory design, and speculative design. While feminist approaches have grown in popularity and become mainstream, it is getting more difficult to distinguish the feminist emancipatory core from other attempts of developing and improving society in various ways. In this workshop, we therefore want to revisit our feminist roots, where theory is a liberatory and creative practice, motivated by affect, curiosity, and wonder. From this standpoint, we consider which of our feminist tools can make a significant difference today, in a highly datafied world. The goal of this workshop is to;1) create an inventory of feminist theories and concepts that have had an impact on our work as designers, educators, researchers, and activists;2) develop a feminist toolbox for the CSCW community to strengthen our feminist literacy. © 2023 Owner/Author.

关键词： Computation theory

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ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environmental Microorganism Image Object Detection

ECA-RetinaNet: A Novel Self-Attention RetinaNet for Environm...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Yang, Hechen Yang, Jinzhu Jiang, Tao Zhao, Xin Zhang, Jinghua Zhao, Peng Chen, Ao Nie, Qianqing Grzegorzek, Marcin Li, Chen Northeastern University Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang China Chengdu University of Traditional Chinese Medicine School of Intelligent Medicine Chengdu China Chengdu University of Information Technology International Joint Institute of Robotics and Intelligent Systems Chengdu China Institute of Medical Informatics University of Luebeck Luebeck Germany

ISBN: (纸本)9798350324457

The detection of environmental microorganisms is always a difficult task, e specially when the multi-scale environment is complex. For tiny objects in microscopic images, current detection methods face the challenge of accurate identification and localization. In contrast, we propose a convolutional neural network (ECA-RetinaNet) for microscopic object detection of which underlying dataset is a high-quality EMDS-7 dataset. The accuracy of ECA-RetinaNet is high, with a high mean Average Precision (mAP) value of 81.42% in the Environmental Microorganisms (EMS) detection task. Its accuracy has been higher than that of the two-stage object detection network. © 2023 IEEE.

关键词： Attention Convolutional Neural Network Environmental Miroorganisms Feature Fusion Object Detection

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The Advanced Encryption Standard with a Dynamic S-Box Using the RC4 Key Schedule 4

The Advanced Encryption Standard with a Dynamic S-Box Using ...

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4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024

作者： Altigani, Abdelrahman Ghalib, Muhammad Rukunuddin Elshoush, Huwaida T. Elsadig, Muawia A. Alrefai, Nashat Naserelden, Shiraz Higher Colleges of Technology Computer Information Science Al Ain United Arab Emirates School of Engineering & Computing De Monfort University Dubai United Arab Emirates University of Khartoum Faculty of Mathematical Sciences and Informatics Computer Science Department Khartoum Sudan College of Computer Science and Information Technology Imam Abdulrahman bin Faisal University Dammam Saudi Arabia College of Science Imam Abdulrahman Bin Faisal University Basic and Applied Scientific Research Center Imam Abdulrahman Bin Faisal University Department of Mathematics Dammam Saudi Arabia Faculty of Science Universiti Teknologi Malaysia Department of Mathematical Sciences Johor Malaysia

ISBN: (纸本)9798350395914

Since the inception of secure communication, safeguarding data confidentiality has been paramount. With the increased reliance on technology, the need for robust security mechanisms is more critical than ever. This paper explores confidentiality in cybersecurity, focusing on symmetric encryption algorithms. While symmetric encryption, particularly the Advanced Encryption Standard (AES), is in wide use, concerns arise regarding its static nature. Therefore, the paper introduces a dynamic cipher, augmenting AES security by introducing a key-dependent S-Box. By leveraging the RC4 key schedule, the proposed cipher generates unique S-Boxes for each encryption key, significantly increasing security against non-generic attacks. Theoretical analysis demonstrates the proposed cipher is capable of resisting brute force, linear cryptanalysis, and differential cryptanalysis. Furthermore, its adaptability to existing AES implementations ensures practicality and ease of adoption. © 2024 IEEE.

关键词： Ciphertext

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