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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Ali Ahmed Omar Amr Ayman Atia Software Engineering Department October University for Modern Sciences and Arts (MSA) Cairo Egypt HCI-LAB Faculty of Computers and Artificial Intelligence Faculty of Computer Science Helwan University October University for Modern Sciences and Arts (MSA) Giza Egypt

ISBN: (数字)9798350367775

ISBN: (纸本)9798350367782

Recovery from substance abuse is a continuous struggle as almost 60% of the addicts at any given instance in time during the recovery process experiences a relapse. The situation worsens during withdrawal, where the process, more so for those recovering from crystal methamphetamine addiction, often presents with intense symptoms that are really fatiguing. Mobile sensors offer a practical solution to continuously monitor these symptoms and provide near real-time insights into an individual's progress towards recovery. This paper explores two deep learning approaches, CNNs and LSTMs(Long Short Term Memory), to identify patterns related to withdrawal symptoms using accelerometer data. It will also address key limitations in current state-of-the-art methods, such as unbiased data collection, feature extraction, and mobile device implementation. Finally, the paper presents personalized reports designed to aid in therapy, helping individuals stay on track and reducing the likelihood of relapse.

关键词： Accelerometers Addiction Medical treatment Crystals Ubiquitous computing Real-time systems Mobile handsets Sensors Gyroscopes Monitoring

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Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression

Expediting In-Network Federated Learning by Voting-Based Con...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Xiaoxin Su Yipeng Zhou Laizhong Cui Song Guo College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology HKSAR

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. To accelerate the communication speed, it has been explored to deploy a programmable switch (PS) in lieu of the parameter server to coordinate clients. The challenge to deploy the PS in FL lies in its scarce memory space, prohibiting running memory consuming aggregation algorithms on the PS. To overcome this challenge, we propose Federated Learning in-network Aggregation with Compression (FediAC) algorithm, consisting of two phases: client voting and model aggregating. In the former phase, clients report their significant model update indices to the PS to estimate global significant model updates. In the latter phase, clients upload global significant model updates to the PS for aggregation. FediAC consumes much less memory space and communication traffic than existing works because the first phase can guarantee consensus compression across clients. The PS easily aligns model update indices to swiftly complete aggregation in the second phase. Finally, we conduct extensive experiments by using public datasets to demonstrate that FediAC remarkably surpasses the state-of-the-art baselines in terms of model accuracy and communication traffic.

关键词： Training Data privacy Quantization (signal) Federated learning Computational modeling Memory management Collaboration

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Frequency-aware Interaction Network for Ultrasound Image Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2024年

作者： Wang, Dongfang Zhou, Tao Zhang, Yizhe Gao, Shangbing Yang, Jian Nanjing University Of Science And Technology Pca Lab Key Lab Of Intelligent Percept. And Syst. For High-Dimensional Information Of Ministry Of Education Jiangsu Key Lab Of Image And Video Understanding For Social Security School Of Computer Science And Engineering Nanjing210094 China Huaiyin Institute Of Technology Faculty Of Computer And Software Engineering Huaian223003 China

Accurate segmentation of medical ultrasound images is crucial for guiding treatment decisions and assessing intervention effectiveness. The challenge of segmenting lesions in ultrasound images arises from factors such as low contrast, high speckle noise, artifacts, and blurred boundaries. Furthermore, this complexity varies significantly among lesions in different cases. While methods based on Convolutional Neural Networks (CNNs) and Transformers have shown promising results in this field, each approach possesses distinct advantages and limitations. To address these challenges, we propose a novel Frequency-aware Interaction Network (FINet). At the core of our FINet lies the proposed Multi-scale Frequency-aware Self-attention (MFS) module, which effectively captures multi-scale feature information within the self-attention layer. This enables our network to model both local and global features, capitalizing on the strengths of both CNNs and Transformers. Additionally, a frequency-aware network is introduced to learn the interactions between spatial locations in the frequency domain to enhance detailed feature representation such as edges. Furthermore, we present a collaborative interactive decoder network, in which a Selective Feature Interaction (SFI) module is proposed to facilitate the semantic and boundary feature interaction, resulting in more precise segmentation outcomes. Experimental results on four medical ultrasound image datasets show the superiority of our FINet over other state-of-the-art segmentation methods. More importantly, our model achieves an excellent trade-off between performance and computational efficiency. © 1991-2012 IEEE.

关键词： Ultrasonic imaging

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Investigating the Relation between Code Comments and Issue Resolution Time in Java Repositories

Investigating the Relation between Code Comments and Issue R...

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IEEE International Conference on Intelligent Computing and Information Systems (ICICIS)

作者： Ahmed Ashraf Hamza Mohammad El-Ramly Manar ElKady M.Sc. in Software Engineering Program Faculty of Computers and Artificial Intelligence Cairo University Cairo Egypt Department of Computer Sciences Faculty of Computers and Artificial Intelligence Cairo University Cairo Egypt

Source code comments play a critical role in software development by facilitating issue resolution and reducing maintenance time and cost. However, the impact of code comments, particularly those aiding program comprehension, on expediting issue resolution remains insufficiently explored. This study investigates the impact of code comments on issue resolution time for issues related to pulling requests involving code maintenance. To this end, ten of the most popular Java repositories on GitHub were selected, and closed pull request-related issues were identified using a specific criterion. Comments were then extracted from classes modified by the pull requests, and a trained BERT model was utilized to classify them according to their usefulness. The percentage of comments relative to code size, the percentage of useful comments relative to all comments, and the average issue resolution time were computed and examined for correlations. The results indicate a negative correlation (-0.68) between the percentage of code comments in modified classes and the average issue resolution time. A stronger negative correlation (-0.73) was found between the percentage of useful code comments to all comments in modified classes and the average issue resolution time per class. These findings demonstrate that code with more comments, particularly useful ones, leads to faster issue resolution. This serves as a basis for further research into the impact of various fine-grained comment types on issue resolution time in different programming languages. Also, it can assist developers in writing more effective comments that accelerate bug-fixing and reduce software maintenance efforts.

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Identification of Sport News in Turkish Tweets Using Deep Learning Architectures

Identification of Sport News in Turkish Tweets Using Deep Le...

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4th International Conference on Intelligent Decision Science, IDS 2020

作者： Toçoğlu, Mansur Alp Onan, Aytuğ Faculty of Technology Department of Software Engineering Manisa Celal Bayar University Manisa45400 Turkey Faculty of Engineering and Architecture Department of Computer Engineering İzmir Katip Çelebi University İzmir35620 Turkey

ISBN: (纸本)9783030665005

In social media and microblogging platforms, it is very popular to share news about sport activities from all around the world. This makes it important to extract information about sports out of text crawled from such platforms. In the scope of this paper, a binary classification is presented to identify tweets containing any information of sports in Turkish. To do so, firstly a dataset, composed of two categories (namely, sport and non-sport), is collected from eleven different Twitter news accounts. Afterwards, a preprocess phase takes place to remove the punctuation marks, the extra spaces, and the numeric characters. In the classification phase, accuracy values of four deep-learning architectures (namely, convolutional neural network, recurrent neural network, gated recurrent unit, and long short-term memory) are calculated to show the classification performances of each architecture. At last, the deep learning classification accuracy values are compared to the most commonly used supervised learning algorithms (namely, Naïve Bayes algorithm, Support Vector Machines, Random Forest, Dense Artificial Neural Network and Decision Tree). © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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Determination of the TOE Factors Influencing the Adoption of Internet Banking Services on SMEs in Yemen: A Moderated Mediation Approach

Determination of the TOE Factors Influencing the Adoption of...

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International Conference on Advances in Electrical and computer Technologies, ICAECT 2021

作者： Al-Fahim, Nabil Hussein Abdulghafor, Rawad Turaev, Sherzod Department of Computer Science Faculty of Information and Communication Technology International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University P.O. Box 15551 Al Ain United Arab Emirates

ISBN: (纸本)9789811911101

The main purpose for carrying out research on the adoption of the Internet banking services is due to the lack of empirical studies on Yemeni Internet banking services. The objective of this study is to explore and investigate the factors of technology–organization–environment theory (TOE) that play the main role in the implemention of Internet banking services by SMEs in Yemen. Four exogenous variables, one mediator, one moderator, and one endogenous variable make up the current study’s framework. The exogenous variables consist of security, relative advantages, government support, top management and organization readiness as mediator as well as organization type is moderator while the endogenous variables comprise of intention toward Internet banking (IB) for banks customers (SMEs). This research also identifies the role of organization readiness as mediating variable that impact SMEs’ intention to adopt Internet banking service and also the role of organization type as a moderator between organization readiness and Internet banking adoption. The current study’s sample included 314 people who were analyzed using partial least square PLS-SEM to look for causal links between the study’s latent variables. The findings showed that security, relative advantages, government support, top management, and organization readiness have a significance and positive effect on intention toward Internet banking adoption among SMEs in Yemen. Furthermore, the findings of the study reveal that organization readiness has a mediating influence on the relationship between TOE factors and intention toward IB adoption among SMEs. Moreover, the results indicate organization type moderates the relationship between organization readiness and intention to adopt IB. In conclusion, the finding shows that SMEs businesses are able to operate more effectively by implementing a broad set of IB products through automating many of their major banking activities and through electronic communication wi

关键词： Moderators

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Identity-based parallel key-insulated proxy signature in the random oracle model

Identity-based parallel key-insulated proxy signature in the...

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2020 International Conference on E-Commerce and Internet Technology, ECIT 2020

作者： Chen, Jianhong Faculty of Computer and Software Engineering HuaiyinInstitute of Technology Huaian China

ISBN: (纸本)9781728159027

Exposure of private keys is harmful to the public key cryptosystems. In this paper, an identity-based parallel key-insulated proxy signature (IBPKIPS) scheme is given to solve the problem of proxy signing key exposure in the identity-based proxy signature system. The IBPKIPS scheme is secure in the random oracle model. Two different helper keys are used to refresh the temporary proxy signing keys in the scheme. The proposed scheme is more flexible than the IBPKIPS scheme in the standard model. © 2020 IEEE.

关键词： Authentication

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An n-MOSFET Layout with Multi-Finger Z Gate for Radiation Tolerance 5

An n-MOSFET Layout with Multi-Finger Z Gate for Radiation To...

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5th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2022

作者： Shan, Chan Liu, Ying Chen, Huan Huo, Dong-Yang Xu, Tian-Rong Jimei University College of Ocean Information Engineering Xiamen China Xiamen Institute of Software Technology Department of Software Technology Xiamen China Minjiang University School of Journalism and Communication Fuzhou China Universiti Malaya Faculty of Computer Science and Information Technology Department of Information Systems Kuala Lumpur Malaysia Universiti Malaya Planning Faculty of Built Environment Department of Urban and Regional Kuala Lumpur Malaysia

ISBN: (数字)9781665479295

ISBN: (纸本)9781665479295

In this paper, a new n-MOSFET layout with multi-finger Z gate is proposed to reduce the total ionizing dose (TID) effect. In addition to the proposed layout, multi-finger single gate layout is also simulated using Sentaurus TCAD in 3D. Firstly, drain current - gate voltage curves of both layouts are compared in order to verify the radiation hardening ability of the proposed layout. Secondly, we compare TID responses by extracting threshold voltages and leakage currents from both layouts. Thirdly, the finger number is investigated for both layouts because it is an important factor for multi-finger layout. The results have indicated that the finger number significantly affects the multi-finger single gate layout, whereas the proposed multi-finger Z gate layout is relatively little affected by the finger number in radiation environment. © 2022 IEEE.

关键词： Drain current

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Task Consistent Prototype Learning for Incremental Few-shot Semantic Segmentation

arXiv

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arXiv 2024年

作者： Xu, Wenbo Wu, Yanan Jiang, Haoran Wang, Yang Wu, Qiang Zhang, Jian Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated examples. Typical incremental approaches encounter a challenge that the objective of the base training phase (fitting base classes with sufficient instances) does not align with the incremental learning phase (rapidly adapting to new classes with less forgetting). This disconnect can result in suboptimal performance in the incremental setting. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Concretely, we mimic the incremental evaluation protocol during the base training session by sampling a sequence of pseudo-incremental tasks. Each task in the simulated sequence is trained using a meta-objective to enable rapid adaptation without forgetting. To enhance discrimination among class prototypes, we introduce prototype space redistribution learning, which dynamically updates class prototypes to establish optimal inter-prototype boundaries within the prototype space. Extensive experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach, offering valuable insights for addressing iFSS challenges. © 2024, CC BY-NC-SA.

关键词： Semantic Segmentation

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Leveraging Smart Video-Based Learning to Enhance Student Engagement and Collaboration

Leveraging Smart Video-Based Learning to Enhance Student Eng...

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Advancements in Computing (ICAC), International Conference on

作者： Chanukya Serasinghe Nashali Perera Devindu Samarasinghe Madusha Muthusinghe Malitha Wijesundara Dept. of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Humanities and Sciences Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

The COVID-19 pandemic has expedited the shift in education to online learning, which has exposed shortcomings in virtual learning environments' personalization and engagement. This research addresses these issues by developing a web application designed to enhance student engagement and learning efficiency. The system has gamified collaboration features, video chapter segmentation, and personalized video recommendation system. The methodology comprises models for generating chapter titles and for precise video transcription, and machine learning algorithms for tailored suggestions depending on user input. Interactive Questions and Answers modules and real-time synchronized video playback are included. The results demonstrate how the system's personalized content, interactive tests, and teamwork tools increase user engagement and comprehension. The efficacy of personalized material and real-time synchronization in enhancing learning results is discussed. This innovative approach offers a comprehensive solution to the challenges of online education, creating a more immersive and interactive learning experience.

关键词： Machine learning algorithms Electronic learning Pandemics Education Natural languages Streaming media Real-time systems Teamwork Synchronization Recommender systems

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