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The Progression of Competencies and Dispositions: How do Maturing software Engineers Compare?

The Progression of Competencies and Dispositions: How do Mat...

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Frontiers in Education (FIE) Conference

作者： Tony Clear Alison Clear Department of Computer Science and Software Engineering Auckland University of Technology Auckand New Zealand School of Computing Eastern Institute of technology Auckland New Zealand

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research paper describes and extends the outcomes from an in- depth study investigating the difference in the expected skills requirements from junior software engineers to senior software engineers, and reflections on the findings from that study. It is a given that senior software engineers have more experience and skills than junior software engineers. However, a focus on their differing competencies and dispositions provides an enhanced mechanism for comparison. Gaps were identified in assessing “professional knowledge” as categorized by the IEEE/ACM computing Curriculum Overview Report (CC2020), and in assessing “dispositions”. It appeared that the specific scenario of comparing the expected competencies between junior and senior software engineers, tested the framework for assessing competencies developed in the CC2020 project and applied in its mapping to the IEEE/ACM Computer Science (CS2013) approved curriculum. In this study into the difference between Junior and Senior software Engineers, an initial review of relevant literature was conducted. The review found that research analyzing job requirements for software engineers of different levels was limited; “experience” as a keyword was seldom mentioned; and a common distinction was made between “soft” and “hard” skills - the latter being skills that were “technical”, such as programming languages, frameworks, libraries, and tools, whereas soft skills referred to skills such as personality traits, attitudes, and teamwork skills. In our extension of that work the notion of soft skills was unpacked into professional skills and dispositions. The process of mapping from the CC2020 competency framework to the CS2013 curriculum had deliberately modelled how to represent a competency-based rather than a knowledge-based curriculum. The critical deficiency identified here was the limitation imposed by adopting a skills framework based on the cognitive taxonomy, and thereby unwittingly omitting the crucial compa

关键词： Computer science Vocabulary Computer languages Reviews Taxonomy Knowledge based systems software Reflection Libraries Teamwork

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Optimized Flame Detection Using a YOLOv5 Neural Network

Optimized Flame Detection Using a YOLOv5 Neural Network

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2024 International Conference on computing, Robotics and System Sciences, ICRSS 2024

作者： Karim, Shahid Ibrar, Muhammad Bilal, Anas Qadir, Akeel Bibi, Irfana School of Information Engineering Xi'an Eurasia University Xi'an710065 China Software College Shenyang Normal University Shenyang China College of Information Science and Technology Hainan Normal University Haikou571158 China Faculty of Computing and Information Technology University of Punjab Department of Computer Science Punjab Lahore Pakistan

ISBN: (纸本)9798331511296

With the rising prevalence of smart homes, there's an increasing demand for comprehensive automation solutions to mitigate fire risks, especially when homeowners are absent or in homes with elderly residents. This study focuses on enhancing flame detection techniques, particularly for smaller, hard-to-detect fire sources. A customized Yolov5 neural network was developed to improve detection precision. This enhancement was facilitated using a dataset of over 2,000 images from lab camera observations and online platforms. These images were then manually labeled and clustered for anchor dimensions using the K-means++ algorithm. The CIOU and diou - nms techniques were integrated to optimize the network's performance. Upon training, the refined model was tested on the Jet Nano, revealing its high efficacy in flame detection. © 2024 IEEE.

关键词： Smart homes

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An Analysis of Multicasting Optimisation Mechanisms for Intelligent Edge computing with Low-Power and Lossy Networks

An Analysis of Multicasting Optimisation Mechanisms for Inte...

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IEEE Conference on Wireless Communications and Networking

作者： Md Israfil Biswas Mohammed Al-Khalidi Muhammad Atif Ur Rehman Byung-Seo Kim Ali Kashif Bashir Department of Computing and Mathematics Manchester Metropolitan University Manchester UK Department of Software and Communications Engineering Hongik University Sejong City Korea

This work studies the built-in multicast model in Contiki OS to provide the basis of a comparative evaluation for a new optimisation model using Radio Duty Cycling (RDC) mechanism. A significant amount of energy is consumed at the edge node executing various multicast routing protocols in Low-Power and Lossy Networks (LLN). The optimisation of the routing protocol and selection of an efficient multicast transmission model has the potential to reduce energy consumption in Edge computing (EC) enabled LLN. With the precise objective of reducing energy consumption, this paper utilises a well-known RDC technique in multicast communication scenarios. To this end, a series of experiments are conducted to evaluate the performance of the existing RDC mechanisms proposed in the literature. The evaluation results are then utilised to develop an efficient RDC-based multicast transmission model. The comparative performance analysis reveals a 23.7% reduction in the RDC rate compared to the traditional model, consequently improving the energy consumption of EC-enabled LLN.

关键词： Energy consumption Analytical models Computational modeling Multicast communication Routing protocols Performance analysis Optimization

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Digital Twin Model Selection for Feature Accuracy in Wireless Edge Networks

Digital Twin Model Selection for Feature Accuracy in Wireles...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Hong Chen Terence D. Todd Dongmei Zhao George Karakostas Department of Electrical and Computer Engineering McMaster University Hamilton Ontario Canada Department of Computing and Software McMaster University Hamilton Ontario Canada

Digital twins (DTs) are virtual implementations of real physical systems (PSs) that interact with other objects on their behalf. Each PS periodically communicates with its digital twin so that the state of the DT is always sufficiently current. Using these updates, a DT can provide features that represent the real behavior of its PS using models that yield differing levels of system accuracy. In this paper, we study the DT model selection problem in wireless networks where the DTs of multiple PSs are hosted at an edge server (ES). The accuracy obtained from a given model is a function of its required amount of PS input data, the updating frequency, and the amount of computational capacity needed at the ES. The objective is to maximize the minimum achieved accuracy among the requested features by making appropriate model selections subject to wireless channel and ES resource availability. The problem is first formulated as an NP-complete integer program. The paper then uses relaxation and dependent rounding, and introduces a polynomial time approximation algorithm to obtain good solutions. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solution.

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EduMAS: A Novel LLM-Powered Multi-Agent Framework for Educational Support

EduMAS: A Novel LLM-Powered Multi-Agent Framework for Educat...

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IEEE International Conference on Big Data

作者： Qiaomu Li Ying Xie Sumit Chakravarty Dabae Lee College of Computing and Software Engineering Kennesaw State University Marietta GA USA Department of Instructional Technology Kennesaw State University Marietta GA USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

In general, educational support with Large Language Models (LLMs) faces challenges in knowledge organization, expertise integration, and contextual adaptation. So, we present EduMAS, a novel multi-agent framework that coordinates specialized agents with graph-based knowledge navigation. Our framework introduces three key innovations: (1) Specialized Agents that provide expertise in different learning aspects to solve decomposed subtasks professionally; (2) Graph Navigator for graph-based knowledge extraction and selection to improve the quality of responses; (3) The Emotional Awareness mechanism for better contextual adaptation. Through comprehensive experiments on college-level physics education and evaluated by six state-of-the-art LLMs, EduMAS demonstrates significant improvements over the baseline model in complex concept integration, cross-disciplinary understanding, and theory-to-application translation. Ablation studies further validate the contribution of each framework component, Specialized Agents and Graph Navigator play important roles in performance improvement. Our work provides strong support for LLM-powered multi-agent system in AI-assisted education.

关键词： Technological innovation Translation Navigation System performance Large language models Education Organizations Physics education Faces Multi-agent systems

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Decoding Diabetes: Unconventional Indicators for Diabetic Diagnosis

Decoding Diabetes: Unconventional Indicators for Diabetic Di...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Sangavi G Kajanan T Methiny S Wishalya Tissera Karthiga Rajendran Faculty of Computing Sri Lanka Institute of Information Technology Malabe Colombo Department of Software Engineering Sri Lanka Institute of Information Technology Malabe Colombo

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Diabetes diagnosis has always involved glucose tests; however, it is possible to recognize diabetic complications by simply looking at skin and nails. This work presents a new approach for identifying diabetes-related complications, including acanthosis nigricans, foot ulcers, and nail infections, utilizing and TensorFlow for deep learning. Both conditions are associated with visual signals and pass through a strictly trained dataset, marked up for maximum accuracy. From the methodologies, including image resizing, data augmentation, and hyperparameter tuning. The findings of accuracy confirm that the platform should register specific characteristics of the problem area and has the essential precision to maintain close attention to possible risks of diabetes and intervene early to reduce the likelihood of patients' deteriorating health.

关键词： Deep learning Visualization Accuracy Image analysis Nails Medical services Data augmentation Skin Diabetes Decoding

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KNN-Based Collaborative Filtering for Fine-Grained Intelligent Grad-School Recommendation System 19th

KNN-Based Collaborative Filtering for Fine-Grained Intellig...

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19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Xu, Jinfeng Liu, Jiyi Ma, Zixiao Wang, Yuyang Wang, Wei Ngai, Edith Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong School of Software Engineering Sun Yat-Sen University Guangdong Zhuhai528406 China Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Computer Science Cornell University IthacaNY14853 United States Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BITUniversity Guangdong Shenzhen518172 China

ISBN: (纸本)9783031651250

The development of the Internet has led to information overload, and how to filter and sift information is a rigorous requirement in all fields. In response to this challenge, recommendation systems have emerged as a valuable tool, offering personalized content and services by efficiently searching and processing dynamically generated information. For students applying to grad schools, finding relevant information can be time-consuming and unreliable from official websites or forums. In light of these challenges, we present a novel solution in the form of an application recommendation platform. Our proposed platform leverages specific open-source datasets and real-time information from platform users using KNN (K-Nearest Neighbor) and CF (Collaborative Filtering) techniques to provide recommendations based on users’ individual backgrounds, we aim to reduce the complexity inherent in information retrieval while simultaneously enhancing the relevance of the recommendations delivered to users. Specifically, we first collect user behavior data, then we will construct the data model and perform some preprocessing on it. Calculate the user similarity, and find out the K-nearest neighbors and rate based on K-nearest neighbors, finally, the recommendation engine is used to calculate the highest-rated items to be recommended to the users. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Collaborative filtering

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DeNC: Unleash Neural Codecs in Video Streaming with Diffusion Enhancement 39

DeNC: Unleash Neural Codecs in Video Streaming with Diffusio...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhou, Qihua Li, Ruibin Guo, Jingcai Huang, Yaodong Xu, Zhenda Cui, Laizhong Guo, Song College of Computer Science and Software Engineering Shenzhen University China Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)157735897X

Recent years have witnessed the rise of Neural-enhanced Video Streaming (NeVS), which integrates neural restoration models into video codecs for higher compression-restoration performance. Despite its benefit, existing work has not well explored the full potential of NeVS paradigm, due to: (1) post-streaming restoration by decoder while lacking the proactive collaboration of encoder, (2) end-to-end optimization based on conventional rate-distortion theory, which has been verified that low distortion is not always a synonym for high perceptual quality, and (3) coupled design for domain-specific tasks that cannot generalize to various video codecs. Observing these limitations, our objective is not to incrementally present an improved restoration model. Instead, we focus on the encoder-decoder synergy, i.e., the codec, which is non-trivial since it inherently strikes the rate-distortion-perception trade-off of NeVS. Aiming at this target, we propose the Diffusion-enhanced Neural Codec (DeNC), a plug- and-play module for current NeVS paradigm, to significantly reduce the required bitrates while preserving high perceptual quality of restored videos. Our key design is twofold. First, DeNC improves the encoder’s compression efficiency by simultaneously reducing the resolution and color bit-depth of frame referencing. Second, DeNC empowers the decoder with perception-oriented restoration capability by making its diffusion-based restoration process aware of the encoder’s compression conditions. Real-world evaluations show that DeNC improves compression ratios with nearly an order of magnitude and achieves much higher restoration quality (e.g., 93+ VMAF and 23% higher MOS) over the latest baselines. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Video streaming

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SACK: Enabling Environmental Situation-Aware Access Control for Vehicles in Linux Kernel

SACK: Enabling Environmental Situation-Aware Access Control ...

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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Chen, Boyan Shen, Qingni Xue, Lei She, Jiarui Zhang, Xiaolei Luo, Xiapu Zhang, Xin Chen, Wei Wu, Zhonghai School of Software and Microelectronic Peking University China Peking University National Engineering Research Center for Software Engineering China School of Cyber Science and Technology Sun Yat-sen University China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9783982674100

Connected and autonomous vehicles (CAVs) operate in open and evolving environments, which require timely and adaptive permission restriction to address dynamic risks that arise from changes in environmental situations (hereinafter referred to as situations), such as emergency situations due to vehicle crashes. Enforcing situation-aware access control is an effective approach to support adaptive permission restriction. Current works mainly implement situation-aware access control in the permission framework and API monitoring in user space. They are vulnerable to being bypassed and are coarse-grained. Autonomous systems have widely adopted mandatory access control (MAC) to configure and enforce system-wide and fine-grained access control policies. However, the MA C supported by Linux security modules (LSM) relies on predefined security contexts (e.g., type) and relatively fixed permission transition conditions (e.g., exec syscall), which lacks consideration of environmental factors. To address these issues, we propose a Situation-aware Access Control framework in the Kernel (SACK), which enforces adaptive permission restriction based on environmental factors for CAVs. Incorporating environmental situations into the LSM framework is not straightforward. SACK introduces situation states as a new security context for abstracting environmental factors in the kernel. Subsequently, SACK utilizes a situation state machine to implement new adaptive permission transitions triggered by situation events. In addition, SACK provides a novel situation-aware policy language that links specific user space permissions to MAC rules while maintaining compatibility with other LSMs such as AppArmor. We develop two prototypes: an independent SACK with its own policies and a SACK-enhanced AppArmor that adaptively updates the corresponding policies of AppArmor. The experimental results demonstrate that SACK can efficiently enforce situation-adaptive permissions with negliaible runtime overh

关键词： Authorization

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DNN-aided Low-complexity Physical-layer Network Coding Enabled Non-orthogonal Multiple Access 19

DNN-aided Low-complexity Physical-layer Network Coding Enabl...

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Chen, Guanyu Chan, Tse-Tin Pan, Haoyuan Liang, Jiaxin Shenzhen University College of Computer Science and Software Engineering Shenzhen China Hang Seng University of Hong Kong Department of Computing Hong Kong Hong Kong Chinese University of Hong Kong Department of Information Engineering Hong Kong Hong Kong

ISBN: (纸本)9781665431620

This paper presents a low-complexity physical-layer network coding (PNC) enabled non-orthogonal multiple access (NOMA) system with the help of deep neural networks (DNN). NOMA allows multiple users to send packets simultaneously to a common access point (AP) using the same frequency band. In PNC-enabled NOMA systems, the AP decodes not only individual packets of different users by conventional multiuser decoding (MUD) techniques, but also different linear combinations of individual packets by PNC decoding, referred to as PNC packets. Prior works showed that the decoded PNC packets could significantly improve the system throughput. However, when the number of simultaneously transmitting users increases, the number of possible PNC packets also increases exponentially, leading to high decoding complexity if the AP tries to blindly decode all possibilities. Therefore, this paper exploits DNNs to reduce the decoding complexity. Specifically, we find that the decoding results of different linear combinations are mainly affected by the relative phase offsets among the wireless signals of different users. Hence, we can use a DNN to learn the relationships between the relative phase offsets and the decoding results. Since our DNN can learn the decoding patterns, i.e., which linear combinations are more likely to be decoded given the same relative phase offsets, the AP can attempt to decode only a subset of all the linear combinations, thus reducing the decoding complexity. Experimental results show that our DNN-assisted decoding scheme reduces the decoding complexity by more than 30% compared with the traditional brute-force approach, while maintaining almost the same throughput performance. © 2022 IEEE.

关键词： Deep neural networks

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