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Fast target-aware learning for few-shot video object segmentation

Science China(Information Sciences) 2022年第8期65卷 71-86页

作者： Yadang CHEN Chuanyan HAO Zhi-Xin YANG Enhua WU Engineering Research Center of Digital Forensics Ministry of EducationSchool of Computer and SoftwareNanjing University of Information Science and Technology School of Education Science and Technology Nanjing University of Posts and Telecommunications State Key Laboratory of Internet of Things for Smart City Department of Electromechanical EngineeringUniversity of Macau State Key Laboratory of Computer Science Institute of SoftwareUniversity of Chinese Academy of Sciences Faculty of Science and Technology University of Macau

Few-shot video object segmentation(FSVOS) aims to segment a specific object throughout a video sequence when only the first-frame annotation is given. In this study, we develop a fast target-aware learning approach for FSVOS, where the proposed approach adapts to new video sequences from its firstframe annotation through a lightweight procedure. The proposed network comprises two models. First, the meta knowledge model learns the general semantic features for the input video image and up-samples the coarse predicted mask to the original image size. Second, the target model adapts quickly from the limited support set. Concretely, during the online inference for testing the video, we first employ fast optimization techniques to train a powerful target model by minimizing the segmentation error in the first frame and then use it to predict the subsequent frames. During the offline training, we use a bilevel-optimization strategy to mimic the full testing procedure to train the meta knowledge model across multiple video *** proposed method is trained only on an individual public video object segmentation(VOS) benchmark without additional training sets and compared favorably with state-of-the-art methods on DAVIS-2017, with a J &F overall score of 71.6%, and on YouT ubeVOS-2018, with a J &F overall score of 75.4%. Meanwhile,a high inference speed of approximately 0.13 s per frame is maintained.

关键词： video object segmentation few-shot target-aware meta knowledge bilevel-optimization

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Deep Learning-Based E-Learning Solution for Identifying and Bridging the Knowledge Gap in Primary Education

Deep Learning-Based E-Learning Solution for Identifying and ...

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International Research Conference on Smart Computing and Systems engineering (SCSE)

作者： D.P.H Arunoda S.R Walpola S.M.I Piumira A.D.M.P Athukorala Thusithanjana Thilakarathna Sanjeevi Chandrasiri Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Educational teaching apps are primarily available in app stores to educate students in various contexts. Lack of educational resources, physical and mental health conditions, and poverty cause some students to skip school and move on to the next school grade without completing the course content of the previous grade. Most of the available apps focus on specific content to cover. The Smart Primary Education Tutor (SPET) teaching app specifically focuses on the missed content by analyzing their knowledge gap and providing lessons to cover the missed content. The main objective of SPET is to develop a methodology to identify the gap in student knowledge and fill the knowledge gap by teaching using smart techniques. SPET is determined to identify students’ interactions (attention, emotions) with the system to identify students’ ability to use the learning tool, identifying gaps in students’ knowledge levels compared to their actual grades using activities and voice-based technologies, teaching to cover the knowledge gap by providing engaging activities and lessons and evaluating students by conducting a final assessment and analyze students’ knowledge and performance obtained through the system. Students between the ages of 5 and 8 are targeted in the community to apply. The solution embeds deep learning-based models including attention classification models using head posture estimation, facial expression recognition, and eye gaze estimation, speech recognition models to identify provided verbal answers, handwriting recognition models to evaluate student performance, and smart teaching. The child emotion recognition model achieved 93% accuracy. The Attention span evaluation model achieved 85% accuracy. The handwritten numerical and English character data recognition model which detects answers for the final assessment paper achieved 85% percent of accuracy.

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Mohsen Tajdinian Behzad Behdani Harold R. Chamorro Vijay K. Sood Hitachi Energy Research Center Hitachi Energy Västerås Sweden Department of Electrical Sustainable Energy Delft University of Technology Delft The Netherlands School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Ontario Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Failures of Induction Motors (IMs) can lead to unscheduled downtime and interruption in industry processes. This paper concentrates on the detection of the stator’s inter-turn faults which are one of the most frequent causes of failures in IMs. The proposed detection method is based on a similarity index that uses the current waveform. To be more specific, the proposed algorithm presents a full-cycle sliding-window-based index based on cosine similarity that only uses current signals for detection of the stator’s inter-turn faults. The proposed index cuts the phase difference before/after the disturbance and, as a result, it only depends on the size variations of the current waveform. The proposed method is technically unaffected by non-fault transient conditions including voltage imbalance, voltage sag, voltage swell, and heavy load changes. The performance of the proposed method is validated with numerous simulated scenarios and has good accuracy and speed of convergence.

关键词： Industries Voltage fluctuations Induction motors Accuracy Fault detection Power quality Stators

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SAMMS: Multi-modality Deep Learning with the Foundation Model for the Prediction of Cancer Patient Survival

SAMMS: Multi-modality Deep Learning with the Foundation Mode...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wen Zhu Yiwen Chen Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Center for Continuing and Lifelong Education National University of Singapore Singapore Faculty of Engineering The University of Sydney Australia

Cancer survival prediction is pivotal in tailoring individualized treatment strategies and guiding clinician decision-making. Yet, existing methodologies grapple with efficiently harnessing the intricate distribution of medical data spanning various modalities. In response, we present SAMMS, an advanced multi-omics multimodal deep learning framework tailored for survival prediction. SAMMS leverages the robust image segmentation model, "Segment Anything" to adeptly characterize pathological images. This prowess is further enhanced by integrating multi-omics data and clinical insights, facilitating holistic modeling across a diverse modal spectrum. The framework weaves a modality-specific subnetwork with a cross-modality common subnetwork, meticulously capturing intra-modality nuances and inter-modality correlations. SAMMS eclipsed its contemporaries by delivering remarkable performance on TCGA’s LGG and KIRC tumor datasets. A battery of analyses underscored SAMMS’s unparalleled capability to distill multifaceted insights from multimodal datasets, yielding richer and more integrative multimodal representations. Such strides promise significant advancements in cancer survival analytics, bolstering the precision and efficacy of patient-centric treatments, disease oversight, and clinical decision processes.

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Cluster Availability Model with Information Update Following Node Recovery

Cluster Availability Model with Information Update Following...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： V. A. Bogatyrev S. V. Bogatyrev V. V. Sivov Department Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation Saint Petersburg Russia Faculty of Software Engineering and Computer Technology ITMO University Saint-Petersburg Russia

A model for evaluating the availability of a fault-tolerant cluster with a constraint on the maximum query service time is proposed. Node recovery in the cluster involves replacing the entire computational node with the latest backup results before the failure. Information update after the backup is achieved through the replication of this information from the working nodes of the cluster. The purpose of the article is to assess the reliability of the cluster and its readiness to timely service requests critical to the waiting time. Restoring up-to-date data in a cluster node after previously entering the data saved during backup into it requires the use of resources of the node in which the data is being restored and the node containing up-to-date data. The specified pair of nodes is excluded from the request servicing process, which reduces the likelihood of timely maintenance. Information recovery in case of failures of all nodes containing replicas of up-to-date data requires more time compared to replication from the memory of a functioning node.

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From Alert to Change: Effects of Sensing on Construction Workers’ Health Protection Behaviours

SSRN

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SSRN 2024年

作者： Zhou, Na Chang-Richards, Alice Dirks, Kim Natasha Wang, Kevin I-Kai Dept. of Civil and Environmental Engineering Faculty of Engineering the University of Auckland New Zealand Engineering Block 5 - Bldg. 405 5 Grafton Rd Auckland Central Auckland1010 New Zealand Engineering Block 1 - Bldg. 401 20 Symonds St Auckland Central Auckland1010 New Zealand Dept. of Electrical Computer and Software Engineering Faculty of Engineering the University of Auckland New Zealand

Compromised air quality on a construction site could lead to significant adverse respiratory impacts on workers. This paper introduces an alert sensing methodology to develop practical health and wellbeing requirements based on the Health Belief Model (HBM)Health Belief Model:(HBM) and the Theory of Planned Behaviour (TPB)Theory of Planned Behaviour:(TPB). The approach leverages the Internet of Things (IoT)Internet of Things:(IoT) to enable real-time, data-driven evaluation of air quality metrics, facilitating the assessment of workers' behavioural change. Dust sensors were mounted on the helmets of 11 construction workers at two New Zealand sites for 21 days, and pre and post-questionnaires were conducted to gather feedback on their experience before and after using the system. The research findings revealed that sensing systems can significantly increase awareness and positive behavioural change amongst construction workers with respect to particulate matter (PM)particulate matter:(PM) exposure. This research expands the HBM and the TPB by demonstrating how perceived health benefits, perceived barriers, self-efficacy, perceived behavioural control, and attitudes influence construction workers' behavioural intentions toward the sensing system. The case study also highlights the potential of sensing technologies to foster healthier behaviours and workplace environments, offering insights for construction companies to develop comprehensive health and wellbeing programs that reduce PM exposure risks and promote proactive worker actions. © 2024, The Authors. All rights reserved.

关键词： Air quality

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Emotional Kansei Words for Digital and Pervasive Product Design

Emotional Kansei Words for Digital and Pervasive Product Des...

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Computing (ICOCO), IEEE International Conference on

作者： Nik Azlina Nik Ahmad Anitawati Mohd Lokman Munaisyah Abdullah Ahmad Iqbal Hakim Suhaimi Software Engineering Section Universiti Kuala Lumpur Malaysian Institute of Information Technology Kuala Lumpur Malaysia Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Shah Alam Malaysia

The explosive growth of pervasive and diverse digital products demands the importance of addressing emotional design within the sphere of product development. As a result, there has been a significant focus from both industry and academia on studying consumer perspectives related to a wide range of pervasive and digital products, adopting a variety of methods, tools, and resources to delve into user emotional experiences. However, a comprehensive study focusing on users' emotions, incorporating a precisely defined semantic or lexicon tailored for this type of user interface (UI) product design remains elusive. Therefore, this study aims to synthesize and validate a comprehensive array of emotional keywords applicable for pervasive and digital products through an extensive literature search and a rigorous validation procedure involving language experts. The result is a meticulously refined set of 175 keywords that will serve as a valuable point of reference for all endeavors associated with evaluating and configuring the emotional structure of pervasive and digital product UI experiences. The study's findings have the potential to provide valuable guidance to industry practitioners and academic researchers, contributing to the improvement of UI design landscape and the development of new systems by enhancing their insight into users' emotional experiences through the utilization of pertinent emotional keywords in a wide range of digital and pervasive UI product designs.

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Reliability of a Reconfigurable Network with Segment Switching

Reliability of a Reconfigurable Network with Segment Switchi...

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Wave Electronics and its Application in Information and Telecommunication Systems (WECONF)

作者： Le Anh Tu V. A. Bogatyrev E. A. Abramova Faculty of Software Engineering and Computer Technology ITMO University Saint Peterburg Russia Department Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation Saint Petersburg Russia

The article proposes models of the structural reliability of a multipath routing network with the possibility of its reconfiguration when switching path segments. The models are focused on optimizing the placement of segment switching nodes, aimed at achieving maximum network performance with the option of connecting request sources to the available communication paths with servers. The study considers the options for connecting each source of requests with one of the paths or with all paths for connecting to servers. The paper shows the existence of an optimal placement of path switching nodes, in which the maximum reliability of the system is achieved.

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An Overview of the Approaches for Generating Test Data in the Context of the Quality Assurance of Big Data Applications

An Overview of the Approaches for Generating Test Data in th...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Daniel Staegemann Matthias Pohl Christian Haertel Christian Daase Mohammad Abdallah Klaus Turowski Faculty of Computer Science Magdeburg Research and Competence Cluster VLBA Otto-von-Guericke University Magdeburg Germany Department of Software Engineering Al-Zaytoonah University of Jordan Amman Jordan

With society’s increasing data production and the corresponding demand for systems that are capable of utilizing them, the big data domain has gained significant importance. However, besides the systems’ actual implementation, their testing also needs to be considered. For this, oftentimes, proper test data sets are necessary. This publication discusses several different approaches how these can be provisioned and, further, highlights the respective advantages, disadvantages, and suitable application scenarios. In doing so, researchers and practitioners that are implementing big data applications and need to test them, or who are generally interested in the domain, are supported in their own considerations on how to obtain test data.

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An Efficient Hardware Accelerator For Lossless Data Compression

An Efficient Hardware Accelerator For Lossless Data Compress...

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International Conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Adel Mahmoud Samuel Medhat Mark Maged Othman Mohamed Reham Karam Khaled Salah M. Watheq El-Kharashi Department of computer and systems Faculty of engineering Ain Shams University Cairo Egypt Siemens Digital Industries Software Fremont USA

ISBN: (纸本)9781665482387

Data compression is a trending field that is used in data storage and data transmission systems. Lossy compression means that data cannot be completely retrieved while in lossless compression the compressed data must be reconstructed exactly. Lossless data compression is used in compressing binary files, telemetry data and high-fidelity medical and scientific images where details are crucial. There is no generic compression algorithm that gives best compression ratio on all data pattern. In this paper, we propose a hybrid lossless hardware architecture that compresses most of data patterns such as repeated data, Gaussian distribution data and images. A profiling-before-compressing and then choosing the right compression hardware is proposed. The proposed design is a highly parallelized architecture that can compress/decompress 64 bytes/cycle with minor overhead. Moreover, it provides high compression ratio on small block sizes as well as large ones.

关键词： Image coding Data compression Signal processing algorithms Memory Signal processing Predictive coding Prediction algorithms

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