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International Conference on Computer, Control, Informatics and Its Applications (IC3INA)

作者： Muhammad Zaki Nur Rahman Esa Prakasa Moch Arif Bijaksana Bachelor of Data Science Faculty of Informatics Telkom University Bandung Indonesia National Research and Innovation Agency (BRIN) Research Center for Data and Information Sciences Bandung Indonesia School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

In research institutions, the ability to monitor and analyze publication output is crucial for assessing productivity and achieving strategic goals. This paper presents a dashboard designed to visualize and analyze publication data from a research center of the Indonesian public institution. The dashboard addresses several key questions: (1) whether the current number of publications is on track to meet the institution’s targets, (2) the comparison between journal and proceeding publications, (3) individual researcher productivity, (4) research group productivity, and (5) the collaborative efforts among researchers and groups. By leveraging this tool, stakeholders can gain insights into publication trends and identify areas for improvement. The findings reveal significant trends in publication types and collaboration patterns, offering a comprehensive overview of the institution’s research output. This study contributes to the understanding of research productivity metrics and their implications for strategic planning in academic research.

关键词： Productivity Measurement Visualization Target tracking Collaboration data visualization Strategic planning Market research Stakeholders Monitoring

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Symptom Dimensions and Cognitive Impairments in Individuals at Clinical High Risk for Psychosis

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Biological Psychiatry: Cognitive Neuroscience and Neuroimaging 2025年第6期10卷 646-655页

作者： Zhang, TianHong Xu, LiHua Wei, YanYan Cui, HuiRu Tang, XiaoChen Hu, YeGang Liu, HaiChun Wang, ZiXuan Chen, Tao Yi, ZhengHui Li, ChunBo Wang, JiJun Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention Shanghai Key Laboratory of Psychotic Disorders Shanghai China Department of Automation Shanghai Jiao Tong University Shanghai China Department of Psychology Shanghai Xinlianxin Psychological Counseling Center Shanghai China Big Data Research Laboratory University of Waterloo Waterloo ON Canada Labor and Worklife Program Harvard University Cambridge Massachusetts United States Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Institute of Psychology and Behavioral Science Shanghai Jiao Tong University Shanghai China

Background: Understanding the intricate relationships between symptom dimensions, clusters, and cognitive impairments is crucial for early detection and intervention in individuals at clinical high risk for psychosis. This study delves into this complex interplay in a clinical high risk sample with the aim of predicting the conversion to psychosis. Methods: A comprehensive cognitive assessment was performed in 744 clinical high risk individuals. The study included a 3-year follow-up period to allow assessment of conversion to psychosis. Symptom profiles were determined using the Structured Interview for Prodromal Syndromes. By applying factor analysis, symptom dimensions were categorized as dominant negative symptoms (NS), positive symptoms-stressful, and positive symptoms-odd. The factor scores were used to define 3 dominant symptom groups. Latent class analysis (LCA) and the factor mixture model (FMM) were employed to identify discrete clusters based on symptom patterns. The 3-class solution was chosen for the LCA and FMM analysis. Results: Individuals in the dominant NS group exhibited significantly higher conversion rates to psychosis than those in the other groups. Specific cognitive variables, including performance on the Brief Visuospatial Memory Test–Revised (odds ratio = 0.702, p = .001) and Neuropsychological Assessment Battery Mazes Test (odds ratio = 0.776, p = .024), significantly predicted conversion to psychosis. Notably, cognitive impairments associated with NS and positive symptoms-stressful groups affected different cognitive domains. LCA and FMM cluster 1, which was characterized by severe NS and positive symptoms-odd, exhibited more impairments in cognitive domains than other clusters. No significant difference in the conversion rate was observed among the LCA and FMM clusters. Conclusions: These findings highlight the importance of NS in the development of psychosis and suggest specific cognitive domains that are affected by symptom dimensions.

关键词： Negative symptoms Neurocognition Subtype Transition Ultra high risk

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An Improved Lightweight CNN_BiLSTM Model for Network Intrusion Detection Using the UNSW-NB15 dataset

An Improved Lightweight CNN_BiLSTM Model for Network Intrusi...

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Computer, Vision and intelligent Technology (ICCVIT), International Conference on

作者： Licheng Zhuang Jun Hu Qingqing Wang Yanyan Wang Kaikai Zhang Sheng Liu School of Computer Science and Technology Huaibei Normal University Huaibei China Anhui Engineering Research Center for Intelligent Computing and Application on Cognitive Huaibei China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

The attack behavior in the network poses a threat to security and privacy issues. In order to improve the effectiveness of IoT network attack detection and compensate for the shortcomings of traditional machine learning methods in detection capabilities, a network intrusion detection method based on lightweight convolutional neural networks (CNN) and bidirectional long short-term memory networks (BiLSTM) is proposed. This method preprocesses data through normalization techniques, extracts data features from both spatial and temporal dimensions using CNN and BiLSTM models, and inputs the extracted spatiotemporal feature vectors into the CNN BiLSTM upper classifier for training, achieving intrusion detection. The proposed model was validated on the dataset UNSW-NB15, and the results showed that the binary and multi classification accuracy of the CNN_BiLSTM model was around 93%. Compared with other methods, the proposed method significantly improves the accuracy of network intrusion detection, and also demonstrates better performance in detection accuracy and false positive rate.

关键词： Training Accuracy Bidirectional long short term memory Network intrusion detection Feature extraction Vectors Spatiotemporal phenomena Convolutional neural networks data mining Security

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Construction and Application of the SMART Model for Adaptive Industrial data Collection Based on Knowledge Graphs

Construction and Application of the SMART Model for Adaptive...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Cheng, Wendan Zhang, Zhen Qian, Heng Wang, Qiuyue Su, Guanqun Meng, Lingge Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China Intelligent Technology Co. Ltd. Jinan China Shandong Standard Institute of Emerging Technologies and Innovations Co. Ltd. Jinan China Shandong Qingniao IIoT Co. Ltd. Jinan China

ISBN: (纸本)9798350362480

Industrial data collection is the foundation for implementing enterprise digitization, which is of great significance to the development of intelligent manufacturing. However, Industrial data Collection Standards (IDCS) are primarily published in paper or PDF formats, which makes it challenging to associate and reuse knowledge. This brings difficulties to data collection and management of heterogeneous devices, thus affecting the adaptive collection of industrial data. For this reason, this paper introduces SMART into industrial data collection and proposes the construction and application of a knowledge graph-based SMART Model. First, ontology semantic reasoning and natural language processing techniques are utilized to develop an ontology model for industrial data collection and extract fine-grained IDCS knowledge. Then, the knowledge is integrated according to the standard primitive structure and conceptual reasoning rules by constructing a standard association model to form the Industrial data Collection Standards Knowledge Graph (IDCS-KG). Finally, the SMART competence level is quantitatively assessed based on the completion degree of each operation within the SMART Model. An experimental case study demonstrates that the SMART Model can collect intelligent adaptive industrial data through the reasoning and analysis of equipment adaptation protocols and data quality management strategies. © 2024 IEEE.

关键词： Knowledge graph

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Universal Speaker Embedding Free Target Speaker Extraction and Personal Voice Activity Detection

arXiv

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arXiv 2025年

作者： Zeng, Bang Li, Ming School of Computer Science Wuhan University Wuhan China Suzhou Municipal Key Laboratory of Multimodal Intelligent Systems Data Science Research Center Duke Kunshan University Kunshan China

Determining "who spoke what and when" remains challenging in real-world applications. In typical scenarios, Speaker Diarization (SD) is employed to address the problem of "who spoke when," while Target Speaker Extraction (TSE) or Target Speaker Automatic Speech Recognition (TSASR) techniques are utilized to resolve the issue of "who spoke what." Although some works have achieved promising results by combining SD and TSE systems, inconsistencies remain between SD and TSE regarding both output inconsistency and scenario mismatch. To address these limitations, we propose a Universal Speaker Embedding Free Target Speaker Extraction and Personal Voice Activity Detection (USEF-TP) model that jointly performs TSE and Personal Voice Activity Detection (PVAD). USEF-TP leverages frame-level features obtained through a cross-attention mechanism as speaker-related features instead of using speaker embeddings as in traditional approaches. Additionally, a multi-task learning algorithm with a scenario-aware differentiated loss function is applied to ensure robust performance across various levels of speaker overlap. The experimental results show that our proposed USEF-TP model achieves superior performance in TSE and PVAD tasks on the LibriMix and SparseLibriMix datasets. Copyright © 2025, The Authors. All rights reserved.

关键词： Embeddings

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Antimicrobial peptide sequence generation based on conditional diffusion model 24

Antimicrobial peptide sequence generation based on condition...

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16th International Conference on Bioinformatics and Biomedical Technology, ICBBT 2024

作者： Qi, Ying Jiang, Xuanpei Jiang, Yongquan Yang, Yan Zhang, Qiangwei Tian, Yuan Department of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan Chengdu China Department of Life Science and Engineering Southwest Jiaotong University Sichuan Chengdu China Department of Computing and Artificial Intelligence Southwest Jiaotong University Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Sichuan Chengdu China Informatization and Network Management Office Southwest Jiaotong University Sichuan Chengdu China

ISBN: (纸本)9798400717666

Antimicrobial peptides (AMPs) play a crucial role in the immune response of organisms to infections, offering an alternative to traditional antibiotic ***, the application of known AMPs has encountered problems in development and *** order to promote the development of AMP-based treatment methods, it is necessary to create a design method with higher precision and selectivity for resistant *** this paper, we propose a method for generating antimicrobial peptides based on a conditional diffusion model, called *** model learns the low dimensional representation of peptides and captures their antimicrobial properties through diffusion *** CDiffusion-AMP separates the learning representation of peptides from their antimicrobial conditions and generates antimicrobial peptides that meet the corresponding conditions through *** results show that this conditional diffusion model method outperforms traditional GAN and VAE models in *** the analysis of the generated sequences, the model can produce reasonable and entirely new AMP sequences after training. © 2024 Copyright held by the owner/author(s).

关键词： Microorganisms

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A Blockchain Consensus Protocol Based on Multidimensional Parameter Credit

A Blockchain Consensus Protocol Based on Multidimensional Pa...

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IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC)

作者： Zexun Jiang Cong Zha Yun Fang Hao Yin Communication University of China School of Data Science and Intelligent Media Beijing National Research Center for Information Science and Technology Tsinghua University

With the rapid development of the industrial Internet, it has greatly improved industrial production efficiency. However, challenges such as information asymmetry and the inade-quacy of a single indicator in supporting supply-demand matching have emerged. To address the challenges, we propose a credit-based consensus protocol for the Industrial Internet. It introduces a credit model that combines multidimensional to achieve supply-demand matching and incorporates regulatory preferences for flexible global regulation. Furthermore, a credit-based Proof-of- Work difficulty adjustment method is proposed to enable distributed Industrial Internet collaboration. The effectiveness of the proposed approach is demonstrated through experiments, and future research directions are discussed.

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Improving multi-view document clustering: leveraging multi-structure processor and hybrid ensemble clustering module 30

Improving multi-view document clustering: leveraging multi-s...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Bai, Ruina Bai, Qi Huang, Ruizhang Chen, Yanping Qin, Yongbin Text Computing & Cognitive Intelligence Engineering Research Center National Education Ministry College of Computer Science and Technology Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China School of Mathematical Sciences Zhejiang University Hangzhou310058 China

ISBN: (纸本)9782493814104

We introduce a multi-view document clustering model called DMsECN (Deep Multi-structure Ensemble Clustering Network), comprising a multi-structure processor and a hybrid ensemble clustering module. Unlike existing models, DMsECN distinguishes itself by creating a consensus structure from multiple clustering structures. The multi-structure processor comprises two stages, each contributing to the extraction of clustering structures that preserve both consistency and complementarity across multiple views. Representation learning extracts both view and view-fused representations from multi-views through the use of contrastive learning. Subsequently, multi-structure learning employs distinct view clustering guidance to generate the corresponding clustering structures. The hybrid ensemble clustering module merges two ensemble methods to amalgamate multiple structures, producing a consensus structure that guarantees both the separability and compactness of clusters within the clustering results. The attention-based ensemble primarily concentrates on learning the contribution weights of diverse clustering structures, while the similarity-based ensemble employs cluster assignment similarity and cluster classification dissimilarity to guide the refinement of the consensus structure. Experimental results demonstrate that DMsECN outperforms other models, achieving new state-of-the-art results on four multi-view document clustering datasets. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Structure (composition)

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A Review of Three-Dimensional Medical Image Visualization

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Health data science 2022年第1期2022卷 83-101页

作者： Liang Zhou Mengjie Fan Charles Hansen Chris R.Johnson Daniel Weiskopf National Institute of Health Data Science Peking UniversityBeijingChina Scientific Computing and Imaging Institute University of UtahSalt Lake CityUSA Visualization Research Center(VISUS) University of StuttgartStuttgartGermany

Importance. Medical images are essential for modern medicine and an important research subject in visualization. However,medical experts are often not aware of the many advanced three-dimensional (3D) medical image visualization techniques thatcould increase their capabilities in data analysis and assist the decision-making process for specific medical problems. Ourpaper provides a review of 3D visualization techniques for medical images, intending to bridge the gap between medicalexperts and visualization researchers. Highlights. Fundamental visualization techniques are revisited for various medicalimaging modalities, from computational tomography to diffusion tensor imaging, featuring techniques that enhance spatialperception, which is critical for medical practices. The state-of-the-art of medical visualization is reviewed based on aprocedure-oriented classification of medical problems for studies of individuals and populations. This paper summarizes freesoftware tools for different modalities of medical images designed for various purposes, including visualization, analysis, andsegmentation, and it provides respective Internet links. Conclusions. Visualization techniques are a useful tool for medicalexperts to tackle specific medical problems in their daily work. Our review provides a quick reference to such techniques giventhe medical problem and modalities of associated medical images. We summarize fundamental techniques and readily availablevisualization tools to help medical experts to better understand and utilize medical imaging data. This paper could contributeto the joint effort of the medical and visualization communities to advance precision medicine.

关键词： Image utilize Medical

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M2F2-Net: Multi-Modal Feature Fusion for Unstructured Off-Road Freespace Detection

M2F2-Net: Multi-Modal Feature Fusion for Unstructured Off-Ro...

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IEEE Symposium on intelligent Vehicle

作者： Hongliang Ye Jilin Mei Yu Hu The Research Center for Advanced Computing Systems Zhejiang Lab Hangzhou China The Research Center for Intelligent Computing Systems Institute of Computing Technology Beijing China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Freespace detection is an important part of autonomous driving technology. Compared with structured on-road scenes, unstructured off-road scenes face more challenges. Multi-modal fusion method is a viable solution to these challenges. But existing fusion methods do not fully utilize the multi-modal features. In this paper, we propose an effective multi-modal network named M2F2-Net for freespace detection in unstructured off-road scenes. We propose a multi-modal feature fusion strategy named Multi-modal Cross Fusion (MCF). MCF module is simple but effective in fusing the features of RGB images and surface normal maps. Meanwhile, a multi-modal segmentation decoder module is designed to decouple the segmentation of two modalities, and it further helps the features of both modalities to be fully utilized. In order to solve the problem that the road edge is difficult to extract in the unstructured scenes, we also propose an edge segmentation decoder module. Extensive experiments show that our approach can lead to significant improvements, which brings 6.1% F1 and 10.8% IoU improvements. Our code will be available at https://***/yhl1010/M2F2-Net.

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