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Emerging Trends in Information technology and engineering (ic-ETITE), Conference on

作者： Tarikwa Tesfa Befikadu Belete Samuel Abera Sudhir Kumar Mohapatra Tapan Kumar Das Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Ethio Telecom Services Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

People are increasingly expressing their views and opinions about a company's goods or service on social media these days. For all types of businesses and organizations, sentiment analysis in text can be used to find prospective customers and obtain quick feedback on the services or goods they provided. As a result, the organization has a better chance and opportunity to meet the needs of the consumer. Using sentiment analysis, we may extract important information from the thoughts and emotions of an organization's supporters and clients. It examines natural language content produced about services and goods. Detailed opinion mining at the aspect/feature level to serve consumer and organizational needs. In this study, three different machine learning algorithms, these are Naïve Bayes (NB), Support Vector Machine (SVM), and K- Nearest Neighbor (KNN) are used to build the proposed aspect-based sentiment analysis model from the customer comments in Amharic text which is collected from Ethio-telecom official Facebook page. The collected data is annotated into sentiment polarity and aspect-based sentiment classes by the help of five annotators. After data annotation, different preprocessing tasks were implemented. Bag of words and TF-IDF word representation techniques were used to represent each unique word to their vector representation. The experiment was conducted on those three models and the result was validated. The labeled aspects were not balanced so that a SMOTE class imbalance handling technique was employed to the dataset. Finally, SVM model trained with TF-IDF word representation technique with SMOTE oversampled dataset has achieved the highest accuracy result of 98% as compared to NB and KNN.

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Optimizing Mobile Robot Localization: Drones-Enhanced Sensor Fusion with Innovative Wireless Communication

Optimizing Mobile Robot Localization: Drones-Enhanced Sensor...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Inam Ullah Deepak Adhikari Habib Khan Shabir Ahmad Christian Esposito Chang Choi Department of Computer Engineering Gachon University Seongnam Republic of Korea University of Electronic Science and Technology of China Chengdu China Department of AI Software Gachon University Seongnam-si South Korea Department of Computer Science University of Salerno Fisciano-Salerno Italy

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Localization is one of the key techniques for 5G/B5G Wireless Sensor Networks (WSNs) research, which determines the application predictions of WSNs to an excessive range. Non-line-of-sight (NLOS) propagation causes most WSN localization errors in complex network environments, like indoors. Extended Kalman Filter (EKF) is a traditional technique that executes inadequately in an NLOS environment, with a focus on the integration of drones or UAVs. The article evaluates the performance of mobile robot localization using EKF in WSN environments. It highlights the importance of improved Internet of Things (IoT) technologies and examines how enhanced 5G communication capabilities and drones or Unmanned Aerial Vehicles (UAVs) effectively overcome obstacles in indoor networks. EKF is a linear estimation of the standard Kalman Filter (KF) and is competent to operate professionally in non-linear classifications. Generally, EKF is based on an iterative method of approximating the statistics of the current state from the previous form. It is also based on the observation framework's linearization around the mean of the existing condition. It has minor calculation difficulty and involves short memory related to the Bayesian procedures, making it appropriate for low-power mobile objects. In this paper, the authors evaluate the localization and tracking presentation of EKF while considering several attributes, such as (a) position, (b) velocity, (c) time, and (d) coverage. The performance of this technique is evaluated using its features and provided with a high level of accuracy.

关键词： Location awareness Wireless communication Wireless sensor networks Sensor fusion Robot sensing systems Mobile robots Kalman filters

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UEMM-Air: Make Unmanned Aerial Vehicles Perform More Multi-modal Tasks

arXiv

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arXiv 2024年

作者： Yao, Liang Liu, Fan Xu, Shengxiang Zhang, Chuanyi Ma, Xing Jiang, Jianyu Wang, Zequan Di, Shimin Zhou, Jun College of Computer Science and Software Engineering Hohai University Nanjing210098 China College of Artificial Intelligence and Automation Hohai University Nanjing210098 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong School of Information and Communication Technology Griffith University NathanQLD4111 Australia

The development of multi-modal learning for Unmanned Aerial Vehicles (UAVs) typically relies on a large amount of pixel-aligned multi-modal image data. However, existing datasets face challenges such as limited modalities, high construction costs, and imprecise annotations. To this end, we propose a synthetic multi-modal UAV-based multi-task dataset, UEMM-Air. Specifically, we simulate various UAV flight scenarios and object types using the Unreal Engine (UE). Then we design the UAV’s flight logic to automatically collect data from different scenarios, perspectives, and altitudes. Furthermore, we propose a novel heuristic automatic annotation algorithm to generate accurate object detection labels. Finally, we utilize labels to generate text descriptions of images to make our UEMM-Air support more cross-modality tasks. In total, our UEMM-Air consists of 120k pairs of images with 6 modalities and precise annotations. Moreover, we conduct numerous experiments and establish new benchmark results on our dataset. We also found that models pre-trained on UEMM-Air exhibit better performance on downstream tasks compared to other similar datasets. The dataset is publicly available (https://***/1e12Leon/UEMM-Air) to support the research of multi-modal tasks on UAVs. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Named Entity Recognition Method for Chinese Public Opinion Based on Multi-Task Learning

Named Entity Recognition Method for Chinese Public Opinion B...

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Science and technology Innovation (IAECST), International Academic Exchange Conference on

作者： Yunkai Zhang Bo Cheng School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunication State Key Laboratory of Networking and Switching Technology (BUPT) Beijing China

Using deep learning to extract public opinion data is a common and effective approach. Nevertheless, many existing named entity recognition models in specific domains [1][2] often depend on hand-engineering, such as constructing external knowledge bases, to improve model performance. Applying these models to different domain data requires the reconstruction of domain-specific knowledge bases, hindering the lateral transferability of the model across various domain tasks. In this paper, we design two subtasks under the framework of multi-task learning to enhance named entity recognition (NER), ultimately proposing a named entity recognition model, MtlTransNER. MtlTransNER employs multi-task learning to enhance not only the performance of named entity recognition but also to avoid complex hand-engineering, thus make the model’s application to data in different domains easier. We conducted experiments on the open-source public opinion dataset [3]. The experimental results demonstrate that our approach achieved an F1 score of 0.6941 on the validation set, outperforming BiLSTM-CRF [4], BERTBiLSTM-CRF [5], BERT-CRF [6] and LLL-WCM [7]. The ablation study also show the effectiveness of each main module in MtlTransNER.

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DCAMM: Dynamic Curve-Based Automated Market Maker

DCAMM: Dynamic Curve-Based Automated Market Maker

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IEEE Conference on Global Communications (GLOBECOM)

作者： Shunrong Jiang Jingwei Chen Fengjiao Li Haijun Geng Haotian Chi The Department of Information Science China University of Mining and Technology Xuzhou China School of Computer and Information Technology Shanxi University Taiyuan China School of Automation and Software Engineering Shanxi University Taiyuan China

Decentralized Exchanges (DEX) allow cryptocurrencies to trade autonomously with each other without involving any centralized financial intermediaries. Among these DEX models, Automated Market Maker (AMM) is most commonly used by major platforms like Uniswap and Curve. However, a typical AMM suffers three main challenges. First, arbitrage trading may cause AMM-based liquidity providers to lose liquidity in assets. Second, adversaries extract on a monthly basis over 10 million USD from AMM traders via sandwich attacks. Third, the volatility of asset prices in AMM may violate the fairness of trading. In this work, we propose a new AMM design, Dynamic Curve-based Automated Market Maker (DCAMM), which utilizes a price oracle with real-time market price feedback to automatically adjust the pool's asset price to match the market price. In DCAMM, there is no space for price manipulation, and traders' slippage losses are converted into equal gains for the liquidity pool. Thus, DCAMM provides the resistance to arbitrage trading and sandwich attacks. Moreover, DCAMM provides a more stable asset price through a strict price adjustment, benefiting traders and safeguarding trading fairness.

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An Exploratory Comparative Study on the Impacts of Technical Support on Student Successes in Computing Project-Based Learning

An Exploratory Comparative Study on the Impacts of Technical...

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Frontiers in Education (FIE) Conference

作者： Ahmad D. Suleiman Jan DeWaters Daqing Hou Yu Liu David Shepherd Computing and Information Sciences Rochester Institute of Technology Rochester NY USA Institute for STEM Education Clarkson University Potsdam NY USA Software Engineering Department Rochester Institute of Technology Rochester NY USA Electrical & Computer Engineering Clarkson University Potsdam NY USA Department of Computer Science Louisiana State University Baton Rouge LA USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

In this research paper, we conducted a comparative study to measure the effectiveness of the provided technical support in computing project-based learning (PjBL) courses. Students learn much better by solving authentic real-world problems through PjBL. PjBL in computing education has proven to boost student motivation and engagement while enhancing academic performance. Crucial to PjBL in computing is the technical support that the instructors can provide to students, which is required for sustained, successful learning during project tasks. Without adequate support, PjBL will fall short of accomplishing its goals, leading to a rise in student frustration, a loss of motivation and engagement, and compromised learning outcomes. Measuring the impacts and effectiveness of the provided support is imperative for fostering continuous improvement, informed decision-making, and student success. It enables instructors to assess the impacts of their strategies, improve their approaches, and utilize their resources more effectively. To measure the impacts of technical support on students during PjBL, we performed a comparative study on two undergraduate computing courses in Spring 2024, Fundamentals of software engineering and Database Systems. In both courses, students work on two assigned projects, one with little and inadequate support and the other with adequate support. We administered a post-survey after each project was completed. We analyzed students' selfreflection responses across four sub-scales, support satisfaction, motivation, self-efficacy, and project satisfaction. The results show a statistically significant increase in the supported project in the Fundamentals of software engineering course and no difference in the Database Systems course. This finding is likely due to other differences between the two projects for the Database Systems course beyond support, such as project scale. Qualitative analysis of students' responses also indicates the need for suppor

关键词： Surveys Statistical analysis Education Decision making Database systems software measurement Continuous improvement Springs software engineering

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Weighted Density for The Win: Accurate Subspace Density Clustering

Weighted Density for The Win: Accurate Subspace Density Clus...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Maixuan Peng Yuyang Wu Yang Lu Mengke Li Yiqun Zhang Yiu-Ming Cheung School of Computer Science and Technology Guangdong University of Technology Guangzhou China Faculty of Science Wilfrid Laurier University Brantford Canada Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science Hong Kong Baptist University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

k-clustering typically struggles with the detection of irregular-distributed clusters due to the natural bias, while density clustering usually cannot well-adapt to different datasets and clustering tasks as it is not an oriented optimization process. This paper, therefore, proposes to perform density clustering in dynamically learned subspaces. To exploit the irregular-distributed clusters obtained by density clustering for the subspace determination, we design a new strategy to appropriately evaluate the importance of attributes. It turns out that the proposed Weighted Density-based Subspace Clustering (WDSC) algorithm inherits the unbiased merits of density clustering, and also upgrades the unlearning density clustering to be learnable under the subspace learning paradigm of k-clustering. A comprehensive evaluation including significance tests, ablation studies, qualitative comparisons, etc., shows the superiority of WDSC.

关键词： Accuracy Heuristic algorithms Signal processing algorithms Clustering algorithms Signal processing Acoustics Speech processing Optimization

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Efficient Privacy Preserving Graph Neural Network for Node Classification

Efficient Privacy Preserving Graph Neural Network for Node C...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xinjun Pei Xiaoheng Deng Shengwei Tian Kaiping Xue School of Computer Science and Engineering Central South University Changsha China School of Software Xinjiang University Wulumuqi China School of Cyber Science and Technology University of Science and Technology of China Hefei China

Graph Neural Networks (GNNs) as an emerging technique have shown excellent performance in a variety of fields, such as social networks and recommendation systems. However, GNNs may have to overcome privacy concerns as large amounts of information about their training datasets may be compromised. In this paper, we develop a privacy-preserving GNN to enforce privacy preservation, which utilizes a private Functional Mechanism (FM) to train the learning model. This mechanism perturbs the polynomial approximation of the objective function to enforce Differential Privacy (DP) in the GNN model. We show that our method can maximize the accuracy of the results with comparable prediction power to the unperturbed results while satisfying the privacy guarantees.

关键词： Training Privacy Differential privacy Frequency modulation Social networking (online) Signal processing Linear programming

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A Scenario Based Approach for Context Query Generation

A Scenario Based Approach for Context Query Generation

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Ravindi de Silva Arkady Zaslavsky Seng W. Loke Prem Prakash Jayaraman Amin Abken Alexey Medvedev School of Information Technology Deakin University Melbourne Australia Department of Computer Sceince and Software Engineering Swinburne University of Technology Melbourne Australia

IoT hardware and application silos have achieved a significant progress in recent years. Yet, they have been unable to realize the full IoT potential, due to the lack of middleware platforms that provide horizontal IoT context sharing and integration. Context Management Platforms (CMPs) enable interoperability between IoT silos, and they provide Context-as- a-Service. CMPs enable real-time context-awareness but often lack appropriate mechanisms to support the evaluation of their performance. This paper proposes a novel scenario-based approach for generating context queries that can be used to assess the performance of a CMP, specifically the caching performance. The proposed approach allows a user to generate realistic context queries based on simulated scenarios. The proposed model has the ability to capture the real-world scenarios and translate them into a sequence of scenes. An ontology-based approach is used for scene modelling. Once the scenes are modelled, the situations are inferred through contextual reasoning. The proposed model reveals that inclusion of context information in the scene makes it feasible to handle uncertainty of situations as expected in the real-world. Based on the situations, context queries are generated using different query templates. To support a fair evaluation of the caching mechanism of a CMP, queries with different complexities are generated from the proposed model.

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Discrimination and Diagnosis of Hypertrophic Cardiomyopathy and Cardiac Amyloidosis via Unimodal Supervision Joint Contrastive Learning

Discrimination and Diagnosis of Hypertrophic Cardiomyopathy ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Xiaofeng Zhang, Hongmei Luo, Xiaoxian Dang, Jiachen Yin, Lixue Peng, Bo Southwest Petroleum University School of Computer Science and Software Engineering Chengdu China Ultrasound and Noninvasive Cardiology Department of Cardiovascular China Key Laboratory of Sichuan Province Ultrasound in Cardiac Electrophysiology and Biomechanics China University of Electronic Science and Technology of China Sichuan Provincial People's Hospital Chengdu China Chengdu University of Traditional Chinese Medicine School of Medical and Life Sciences Chengdu China

ISBN: (纸本)9798350386226

Hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) are both heart diseases. Their echocardiographic presentations are similar, whereas the latter is rare in clinical practice, making them susceptible to misdiagnosis. Recently, deep learning-based echocardiographic diagnostic methods have been proposed. These data-driven methods require a large amount of data for training to improve diagnostic accuracy. Therefore, these methods do not apply to conditions with sparse clinical data, such as CA. To address the problem of poor model performance due to few training data, we propose a multimodal model (USCL) combining multiview and text to differentiate between HCM and CA. Specifically, the USCL consists of two main parts: one part employs unimodal supervision (UMS) to obtain echocardiogram and text respectively, the relevant unimodal representations and prediction results. The other part uses a designed multimodal contrast learning (MMCL) approach to tune the representations of the two modalities, thus exploring more reliable multimodal representations in few-shot learning. We build a dataset for training and evaluation which includes 209 patients with HCM and 203 patients with CA, as well as 200 patients with normal cardiac function. Experiments show that USCL achieves promising results. © 2024 IEEE.

关键词： Contrastive Learning

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