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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Su, Ran Xiao, Jin Cui, Hui Xuan, Ping Feng, Xikang Wei, Leyi Jin, Qiangguo Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Shantou University School of Engineering Department of Computer Science China Northwestern Polytechnical University School of Software China Macao Polytechnic University AIDD Faculty of Applied Science China Xiamen University School of Information Xiamen China Yangtze River Delta Research Institute Northwestern Polytechnical University Taicang China

ISBN: (纸本)9798350386226

Gliomas hold a prominent position in neurooncology due to their high malignancy and poor survival rates. Accurately predicting the prognosis and survival risk of glioma patients is crucial for clinical treatment. Recent advances in survival prediction methods emphasize the importance of integrating complementary information from diverse modalities while neglecting the significant modality gap between pathological images and genomic data. To address this issue, we propose a modality-specific knowledge interaction network (MSKI-Net), which integrates whole slide images (WSI), RNA-Seq gene expression data, and copy number variation (CNV) data for glioma survival analysis. The MSKI-Net consists of a modality-specific feature enhancement (MSFE) module, a modality-interactive cross-attention (MICA) module, and a modality-specific knowledge-guided representation learning (MSKR) module. The three modules collaborate by complementing modality-specific features with modality-agnostic knowledge to improve the learning capability of MSKI-Net. Furthermore, we construct a dataset named TCGAmm, which combines WSI, RNA-Seq, and CNV data from The Cancer Genome Atlas (TCGA) to address the issue of data scarcity. Extensive experiments demonstrate that MSKI-Net achieves superior performance in predicting the survival risk of glioma cancer. © 2024 IEEE.

关键词： Gene expression

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A Clustering-Aided Optimization Algorithm for Antenna Beamforming in Multi-Cell HAPS systems

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IEEE Internet of Things Journal 2025年

作者： Dai, Wei Yang, Siyuan Yin, Yue Bouazizi, Mondher Ohtsuki, Tomoaki Keio University Graduate School of Science and Technology Kanagawa223-8522 Japan Keio University Faculty of Science and Technology Department of Information and Computer Science Kanagawa223-8522 Japan

High altitude platform station (HAPS) systems have emerged as a key solution to address the increasing networking demands of the Internet of Things (IoT), providing wide-area coverage, low latency, enhanced network resilience, and cost-effective service delivery, particularly in remote regions. Given that the continuous movement of HAPS and the inherent mobility of user equipments (UEs) often lead to low and unevenly distributed UE throughput, it is crucial for HAPS systems to dynamically control the antenna using beamforming techniques. However, the current reactive approaches to dynamic control fail to effectively minimize the number of low throughput UEs and achieve low time complexity. To overcome these challenges, we propose a clustering-aided particle swarm optimization (PSO) algorithm to determine the antenna parameters, enabling HAPS to configure multiple cells and dynamically control beams based on UE distribution. This algorithm leverages UE clustering information to redefine the search space, reducing the complexity while enhancing the ability to find the global optimum. Specifically, we propose a novel regulated K-means algorithm that groups UEs into appropriately balanced clusters, precisely reducing the search space for global optimization. Simulations using real-world UE distributions demonstrate that our proposed method outperforms conventional approaches in reducing low throughput UEs and providing balanced throughput distribution, while maintaining low computational complexity. © 2014 IEEE.

关键词： Particle swarm optimization (PSO)

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Enhancing Ontology Translation Through Cross-Lingual Agreement 48

Enhancing Ontology Translation Through Cross-Lingual Agreeme...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Tian, Mingjie Giunchiglia, Fausto Song, Rui Chen, Xing Xu, Hao Jilin University School of Artificial Intelligence Changchun130012 China University of Trento Department of Information Engineering and Computer Science Italy Jilin University College of Computer Science and Technology Changchun130012 China

ISBN: (纸本)9781728163277

Ontology serves as the foundation for the underlying representation of knowledge. In order to achieve the sharing of knowledge across languages, ontologies that are typically only represented in English must be translated into different languages. Building a domain-specific translation system is necessary for ontology due to the extremely specialized vocabulary and absence of contextual information. In this paper, we introduce cross-lingual agreement to alleviate the aforementioned issues. We propose a method for representing ontology labels that constructs agreement constraint objects by minimizing the distances between source and target sides. We also integrate with adversarial learning to reduce the difference between the ontology label and the hypothesis generated by the translation model during the fine-tuning. Finally, the agreement modeling strategy is incorporated into the decoding phase to guide the generation of translation candidates. Experiments on the four domains English-to-German ontology show that the proposed method achieves significant improvements over the baselines. © 2023 IEEE.

关键词： adversarial learning cross-lingual agreement cross-lingual alignment fusion decoding ontology translation

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Optimization-Based Collision Avoidance in Underwater Wireless Sensor Network 1

Optimization-Based Collision Avoidance in Underwater Wireles...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Sugumaran, S. Buvaneswari, B. Senthilkumar, K.S. Jeevitha, R. Ramachandra, A.C. Vishnu Institute of Technology ECE Department Bhimavaram India Panimalar Engineering College Department of Information Technology Chennai India Sri Ramakrishna College of Arts & Science Department of Computer Applications Coimbatore India KPR Institute of Engineering and Technology Computer Science and Engineering Arasur India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350323795

The Underwater Wireless Sensor Network (UWSN) is crucial for private, military, and commercial maritime operations, like environmental monitoring, underwater research, scientific data collection, and underwater wireless communications. The existing protocols for Terrestrial Wireless Sensor Networks (TWSNs) could be better in terms of power efficiency, reliability, and effectiveness. Due to their unique characteristics. The energy-efficient convergent depth variation routing (ECDVR) algorithm is introduced to evaluate and reduce power consumption by calculating the distance between each node point and node depth variation. This algorithm considers the two-hop neighbour depth variation when sending data to a neighbor node with a higher forward angle toward the receiver. It calculates the receive time contribution of the Adaptive Time Difference of Arrival (ATDoA). Adaptive Time Difference of Arrival (ATDoA) The arrival time of a signal makes it simple to estimate the time difference between two signals at a node. The storage node-to-receiver approach is the one that has been proposed for determining the node's remaining power for each information exchange. In the subsequent UWSN transmission, the sink allocates the node's higher power when the node reduces its power. Extending the packet's lifetime and shortening the time it takes to reach its destination boosts system performance. It outperforms the compared algorithms regarding node lifetime, dead node count, power consumption, and propagation delay. © 2023 IEEE.

关键词： Energy efficiency

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Deep Learning Networks for Human Knee Abnormality Detection Based on Surface EMG Signals 47

Deep Learning Networks for Human Knee Abnormality Detection ...

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47th International Conference on Telecommunications and Signal Processing, TSP 2024

作者： Mekruksavanich, Sakorn Phaphan, Wikanda Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Research Group in Statistical Learning and Inference King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok Thailand Intelligent and Nonlinear Dynamic Innovations Research Center King Mongkut's University of Technology North Bangkok Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350365597

Early knee problem management relies on precise identification and classification of abnormalities. Surface electromyography (sEMG) and goniometer signals offer non-invasive screening for muscle activity and joint angle patterns, yet their complexity poses challenges in extracting critical diagnostic information. This paper proposes a novel deep-learning method using sEMG and goniometer data for knee abnormality diagnosis. The proposed ResNeXt model, employing CNNs and multi-kernel modules, is evaluated on the UCIEMG dataset. Experimental results demonstrate ResNeXt's superior accuracy, precision, recall, and F1-score compared to baseline models (CNN and LSTM). Res NeXt achieves the best performance with combined EMG and goniometer data, reaching 96.37% accuracy and 93.77% F1-score, with fewer trainable parameters, indicating computational efficiency. The findings indicate ResNeXt's effectiveness in identifying knee abnormalities using biosensor data, particularly sEM G and goniometer signals, aiding early disease detection and treatment. © 2024 IEEE.

关键词： Learning systems

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HY-PC:Enabling Consistent Positioning and Communication Using Visible Light

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China Communications 2023年第4期20卷 180-194页

作者： Xiangyu Liu Yaoheng Chen Lei Guo Song Song School of Information Science and Engineering Shenyang Ligong UniversityShenyang 110158China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Hangzhou Institute of Advanced Technology Hangzhou 310056China Institute of Intelligent Communications and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China

Visible light(VL)plays an important role in achieving high-precision positioning and low bit error radio(BER)data ***,most VL-based systems can not achieve positioning and communication,*** are two problems:1)the hybrid systems are difficult to extract distinguishable positioning beacon features without affecting communication performance,2)in the hybrid systems,the lost data bits in the inter-frame gap(IFG)are hard to recover,which affects positioning and communication ***,in this article,we propose a novel VL-based hybrid positioning and communication system,named HY-PC system,to solve the above ***,we propose the robust T-W mapping for recognizing specific Light Emitting Diodes(LEDs),which can provide stable LED recognition accuracy without adding extra beacon data and does not decrease the communication ***,we also propose the novel linear block coding and bit interleaving mechanism,which can recover the lost data bits in the IFG and improve data communication ***,we use commercial off-the-shelf devices to implement our HY-PC system,extensive experimental results show that our HY-PC system can achieve consistent high-precision positioning and low-BER data communication,simultaneously.

关键词： visible light positioning visible light communication T-W Mapping linear block coding bit interleaving

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Synergy of machine learning and Mie-tronics: tailoring resonant response of meta-atom on demand 28

Synergy of machine learning and Mie-tronics: tailoring reson...

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Integrated Optics: Devices, Materials, and Technologies XXVIII 2024

作者： George, Renee C. Sedeh, Hooman Barati Lai, Fangxing Li, Hao Li, Wenhao Gao, Jiannan Sun, Jingbo Xiao, Shumin Kuznetsov, Arseniy Litchinitser, Natalia M. Department of Electrical and Computer Engineering Duke University DurhamNC United States Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System Harbin Institute of Technology Shenzhen Shenzhen518055 China School of Materials Science and Engineering Tsinghua University Beijing100084 China A*STAR Institute Singapore Singapore

ISBN: (纸本)9781510670389

Optical metasurfaces are engineered 2D electromagnetic structures enabling flat optical elements with properties not readily found in nature. Their unit cells, meta-atoms, usually are represented by a set of electric and magnetic multipoles. All-dielectric-based metasurfaces have recently attracted significant attention owing to their virtually lossless transmission properties at optical frequencies. A majority of reported dielectric metamaterials are composed of relatively simple meta-atoms such as spheres, cubes, and cylinders, whose electromagnetic response is dominated by the electric dipole. However, magnetic dipoles and higher-order multipoles may enable new optical properties and functionalities, including directional scattering, beam steering, and new frequency generation. Despite impressive progress in the field of optical metamaterials and nanofabrication technologies, engineering meta-atoms that support such higher-order resonances is still challenging. Here, we demonstrate that designed titanium dioxide meta-atoms can enable dominant magnetic dipole response. We apply a machine-learning model to predict a meta-atom shape with a strong magnetic dipole resonant mode at the operating wavelength of 750 nm. Using finite-element-based numerical simulations implemented in COMSOL Multiphysics, we found that the optimized meta-atom is robust against experimental variations and conditions such as a non-perfectly collimated incident beam, nanofabrication inaccuracies, and an added substrate. The meta-atoms have been fabricated using two approaches, focused ion beam lithography and an electron beam lithography followed by reactive ion etching, and characterized using white light spectroscopy. To the best of our knowledge, this is the first experimental realization of a machine-learning-based optimization of a magnetic dipole mode at optical frequencies. © 2024 SPIE.

关键词： Atoms

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A Unified Conceptual Model Based on Business Artifacts for information systems 24

A Unified Conceptual Model Based on Business Artifacts for I...

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24th International Arab Conference on information technology, ACIT 2023

作者： Assaf, Maroun Abi Fares, Charbel Rachini, Ali Badr, Youakim Barbar, Kablan Holy Spirit University of Kaslik Department of Computer Science and Information Technology Jounieh Lebanon School of Graduate Professional Studies The Pennsylvania State University United States Lebanese University Department of Computer Science Jdeideh Lebanon

ISBN: (纸本)9798350384307

Conceptual modeling captures information systems requirements and specifications in a graphical representation at a high-level without including low-level implementation details. Entity Relationship Model (ER Model) and Business Process Model and Notation (BPMN) are two major conceptual models for modeling respectively data and process aspects of an information system. On the other hand, Conceptual Artifact Model (CAM) is an alternative conceptual model for modeling both data and process aspects in a single model based on the notion of Artifacts. In this article, we formalize the CAM and define algorithms for generating both Database and Workflow specifications from this model. These specifications are then used to create the Relational Database and execute the Workflow of the information system. Benefits of such alternative modeling approach are not limited to reducing the time and effort of designing information systems but also of implementing them. © 2023 IEEE.

关键词： Artifact-centric Modeling Business Process Modeling Data Modeling Database information systems IT in Business Process Modeling Software Engineering

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Investigating the Transferability of Evasion Attacks in Network Intrusion Detection systems Considering Domain-Specific Constraints 1

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25th International Conference on information Security Applications, WISA 2024

作者： Mbow, Mariama Roman, Rodrigo Seal, Ayan Wang, Kevin I-Kai Mohanty, Sraban Kumar Sakurai, Kouichi Department of Information Science and Technology Kyushu University Fukuoka Japan Lab University of Malaga Málaga Spain PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur482005 India Department of Electrical Computer and Software Engineering The University of Auckland Auckland1010 New Zealand

ISBN: (数字)9789819616244

ISBN: (纸本)9789819616237

Recently, adversarial attacks against machine learning-based network intrusion detection systems (NIDS) have gained significant attention in cybersecurity. This study investigates the transferability of these adversarial attacks in the context of NIDS. In addition, most existing studies in adversarial learning against NIDS adopted attack strategies designed originally for image classification without considering network traffic characteristics. However, this is impractical and will fail in the real world as network traffic features are constrained, and ignoring the functional behavior of the network traffic features leads to invalid network traffic flow or produces adversarial samples that do not retain their original functionality (malicious or benign). To address these issues, we propose a constrained momentum iterative fast gradient sign method (C-MIFGSM) to generate adversarial network flows that can successfully evade an ML-based IDS through transfer-based attacks while preserving the functional behavior of the network traffic. Our approach was validated using several target NIDS models built with the NSLKDD benchmark dataset. Experimental results demonstrate that even without knowledge of the target model and under feature constraints, it is possible to generate adversarial network traffic flows that achieve a high evasion attack success rate against NIDS built with deep learning and classical ML models. For example, the attack degraded the detection rate of the DoS traffic drops from 92.35% to 20.27% for the MLP model with an evasion increase rate of 78.04%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Unsupervised Approach for Email Spam Filtering using Data Mining

EAI Endorsed Transactions on Energy Web

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EAI Endorsed Transactions on Energy Web 2021年第36期8卷 1-6页

作者： Manaa, Mehdi Ebady Obaid, Ahmed J. Dosh, Mohammed Hussein Department of Information Networks College of Information Technology University of Babylon Iraq Department of Computer Science Faculty of Computer science and Mathematics University of Kufa Iraq College of Education for Girls University of Kufa Iraq

The computer networks overwhelm with unwanted emails, which are called spam emails. This email brings financial damage to companies and losses of user reputation. In this paper, the increasing volume of these emails has created the intense need to design and implement robust anti-spam filtering using the vector space model and Machine Learning (ML). ML algorithms have successfully used to detect and filter spam emails that jeopardize the network resources and consume the bandwidth. The main objective is to apply unsupervised learning M-DBSCAN to classify spam and ham emails. A robust method using the Modified Density-Based Spatial Clustering of Applications with Noise (M-DBSCAN) is implemented. The extracted N-representative points from each cluster are applied in the online test. These points represent the cluster objects to detect spherical and non-spherical clusters. These N-representative points are formed from the training step to detect spam email using distance measures. The data set used from the Kaggle website included many objects of ham and spam emails. The results show good performance accuracy with 97.848% in M-DBSCAN compared with 95.918% for standard DBSCAN accuracy and efficient values in false-negative rate, false-positive rate, f-score and online time detection. © 2021,EAI Endorsed Transactions on Energy *** Rights Reserved.

关键词： Machine learning

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