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arXiv 2025年

作者： Lengauer, Stefan Shao, Lin Miri, Hossein Bedek, Michael Kupfer, Cordula Zangl, Maria Kubicek, Bettina Dienstbier, Barbara Jeitler, Klaus Krenn, Cornelia Semlitsch, Thomas Zipp, Carolin Albert, Dietrich Siebenhofer, Andrea Schreck, Tobias Institute of Visual Computing Graz University of Technology Austria Fraunhofer Austria Center for Data Driven Design Austria International School of Engineering Chulalongkorn University Thailand Institute of Psychology University of Graz Austria Institute of General Practice and Evidence-based Health Services Research Medical University of Graz Austria Institute for Medical Informatics Statistics and Documentation Medical University of Graz Austria Institute for General Practice Goethe University Frankfurt am Main Germany

The effective and targeted provision of health information to consumers, specifically tailored to their needs and preferences, is indispensable in healthcare. With access to appropriate health information and adequate understanding, consumers are more likely to make informed and healthy decisions, become more proficient in recognizing symptoms, and potentially experience improvements in the prevention or treatment of their medical conditions. Most of today’s health information, however, is provided in the form of static documents. In this paper, we present a novel and innovative visual health information system based on adaptive document visualizations. Depending on the user’s information needs and preferences, the system can display its content with document visualization techniques at different levels of detail, aggregation, and visual granularity. Users can navigate using content organization along sections or automatically computed topics, and choose abstractions from full texts to word clouds. Our first contribution is a formative user study which demonstrated that the implemented document visualizations offer several advantages over traditional forms of document exploration. Informed from that, we identified a number of crucial aspects for further system development. Our second contribution is the introduction of an interaction provenance visualization which allows users to inspect which content, in which representation, and in which order has been received. We show how this allows to analyze different document exploration and navigation patterns, useful for automatic adaptation and recommendation functions. We also define a baseline taxonomy for adapting the document presentations which can, in principle, be leveraged by the observed user patterns. The interaction provenance view, furthermore, allows users to reflect on their exploration and inform future usage of the system. Copyright © 2025, The Authors. All rights reserved.

关键词： Electronic health record

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Using Fitness Dependent Optimizer for Training Multi-layer Perceptron

arXiv

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arXiv 2022年

作者： Abbas, Dosti Kh. Rashid, Tarik A. Abdalla, Karmand H. Bacanin, Nebojsa Alsadoon, Abeer Faculty of Engineering Soran University Kurdistan Iraq Computer Science and Engineering Department University of Kurdistan Hewler Kurdistan Iraq Education and Psychology Department College of Education University of Raparin Kurdistan Iraq Faculty of Informatics and Computing Singidunum University Belgrade11000 Serbia School of Computing and Mathematics Charles Sturt University Sydney Australia School of Computing Engineering and Mathematics Western Sydney University Sydney City Campus Australia Sydney Australia Kent Institute Australia Information Technology Department Sydney Australia

This study presents a novel training algorithm depending upon the recently proposed Fitness Dependent Optimizer (FDO). The stability of this algorithm has been verified and performance-proofed in both the exploration and exploitation stages using some standard measurements. This influenced our target to gauge the performance of the algorithm in training multilayer perceptron neural networks (MLP). This study combines FDO with MLP (codename FDO-MLP) for optimizing weights and biases to predict outcomes of students. This study can improve the learning system in terms of the educational background of students besides increasing their achievements. The experimental results of this approach are affirmed by comparing with the Back-Propagation algorithm (BP) and some evolutionary models such as FDO with cascade MLP (FDO-CMLP), Grey Wolf Optimizer (GWO) combined with MLP (GWO-MLP), modified GWO combined with MLP (MGWO-MLP), GWO with cascade MLP (GWO-CMLP), and modified GWO with cascade MLP (MGWO-CMLP). The qualitative and quantitative results prove that the proposed approach using FDO as a trainer can outperform the other approaches using different trainers on the dataset in terms of convergence speed and local optima avoidance. The proposed FDO-MLP approach classifies with a rate of 0.97. © 2022, CC BY.

关键词： Multilayer neural networks

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DNAP: Dynamic Nuchwezi architecture platform - A new software extension and construction technology

TechRxiv

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TechRxiv 2020年

作者： Lutalo, Joseph Willrich Steven Eyobu, Odongo Kanagwa, Benjamin Department of Networks School of Computing and Informatics Technology Makerere University Kampala Uganda

The need to improve or build new software systems to solve new and old business challenges is a persistent challenge in the software consumer and development industry, yet costly. To minimize these costs, the construction method should be designed with the following qualities in mind;software portability, extensibility, and simplicity. To achieve these qualities, this paper proposes the Dynamic Nuchwezi Architecture Platform (DNAP), which is a new software construction and extension technology. DNAP offers a visual programming paradigm with a capability of generating production-ready business automation software for both mobile and web. It also offers a simple mechanism for the extension of existing softwares using embeddable components. To evaluate and justify DNAP, eight Software Operating Environment (SOE) metrics have been developed and together with the SOE model, are used to contrast DNAP against four alternative software construction technologies namely;Android Platform, .NET Framework, Java SE Platform and Python. The performance evaluation results show that DNAP offers an average of 33% reduction in software construction complexity and an 11% enhancement in language efficiency when compared to alternative technologies. © 2020, CC BY.

关键词： Memory architecture

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Bootstrapping Simulation in Determining the Prognostic Factors of Lung Cancer Disease by Parametric Survival Analysis

Bootstrapping Simulation in Determining the Prognostic Facto...

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computing (ICOCO), IEEE International Conference on

作者： Siti Afiqah Muhamad Jamil Mohd Asrul Affendi Abdullah Nurain Ibrahim Mahayaudin M. Mansor Nor Azura Md Ghani School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Malaysia Faculty of Applied Sciences and Technology Universiti Tun Hussein Onn Malaysia Pagoh Campus Johor Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Kompleks Al-Khawarizmi Universiti Teknologi MARA Shah Alam Selangor Malaysia

Big data analytics focuses on getting useful insights, trends and pattern out of complex and large data. Increasing the sample by resampling the data, in biostatistics expertise, can be employed using the bootstrapping techniques. The world of bootstrapping is very large and expanding where it does not only compute the confidence interval but also perform a standard resampling method. Nevertheless, survival analysis study mostly allows the data to be not normally distributed because of the censored observations. Small number of samples also one of the reasons why this study has to perform bootstrapping to overcome the issues of biasness. Bootstrapping method is said to be one of the best methods in handling skewed data. Thus, by considering bootstrapping method, this study aims to find the most significant prognostic factors of lung cancer disease that affect the survival times with the presence of censored observations by using the parametric survival analysis. Therefore, based on 100, 150, 250 and 600 number of sampling sizes, exponential distribution appeared to fit all the assigned sample sizes. Weibull and log-logistic distribution seems to fit the data only for 100 number of samples. Races and two of the interaction terms in the model appeared to be the most significant prognostic factors affecting the survival time of lung cancer.

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High-rate quantum key distribution exceeding 110 Mb/s

arXiv

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arXiv 2023年

作者： Li, Wei Zhang, Likang Tan, Hao Lu, Yichen Liao, Sheng-Kai Huang, Jia Li, Hao Wang, Zhen Mao, Hao-Kun Yan, Bingze Li, Qiong Liu, Yang Zhang, Qiang Peng, Cheng-Zhi You, Lixin Xu, Feihu Pan, Jian-Wei Hefei National Research Center for Physical Sciences The Microscale School of Physical Sciences University of Science and Technology of China Hefei230026 China Shanghai Research Center for Quantum Science CAS Center for Excellence in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China School of Cyberspace Science Faculty of Computing Harbin Institute of Technology Harbin150080 China Jinan Institute of Quantum Technology Shandong Jinan250101 China

Quantum key distribution (QKD) can provide fundamentally proven security for secure communication. Toward application, the secret key rate (SKR) is a key figure of merit for any QKD system. So far, the SKR has been limited to about a few megabit-per-second (Mb/s). Here we report a QKD system that is able to generate key at a record high SKR of 115.8 Mb/s over 10-km standard fibre, and to distribute key over up to 328 km of ultra-low-loss fibre. This attributes to a multi-pixel superconducting nanowire single-photon detector with ultrahigh counting rate, an integrated transmitter that can stably encode polarization states with low error, a fast post-processing algorithm for generating key in real time and the high system clock-rate operation. The results demonstrate the feasibility of practical high-rate QKD with photonic techniques, thus opening its possibility for widespread applications. © 2023, CC BY.

关键词： Quantum cryptography

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OMPQ: Orthogonal Mixed Precision Quantization

arXiv

引用

arXiv 2021年

作者： Ma, Yuexiao Jin, Taisong Zheng, Xiawu Wang, Yan Li, Huixia Wu, Yongjian Jiang, Guannan Zhang, Wei Ji, Rongrong Media Analytics and Computing Lab Department of Artificial Intelligence School of Informatics Xiamen University China Media Analytics and Computing Lab Department of Computer Science and Technology School of Informatics Xiamen University China Peng Cheng Laboratory Shenzhen China Samsara SeattleWA United States Tencent Youtu Lab Shanghai China CATL China

To bridge the ever-increasing gap between deep neural networks' complexity and hardware capability, network quantization has attracted more and more research attention. The latest trend of mixed precision quantization takes advantage of hardware's multiple bit-width arithmetic operations to unleash the full potential of network quantization. However, existing approaches rely heavily on an extremely time-consuming search process and various relaxations when seeking the optimal bit configuration. To address this issue, we propose to optimize a proxy metric of network orthogonality that can be efficiently solved with linear programming, which proves to be highly correlated with quantized model accuracy and bit-width. Our approach significantly reduces the search time and the required data amount by orders of magnitude, but without a compromise on quantization accuracy. Specifically, we achieve 72.08% Top-1 accuracy on ResNet-18 with 6.7Mb parameters, which does not require any searching iterations. Given the high efficiency and low data dependency of our algorithm, we use it for the post-training quantization, which achieves 71.27% Top-1 accuracy on MobileNetV2 with only 1.5Mb parameters. Copyright © 2021, The Authors. All rights reserved.

关键词： Linear programming

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SeqGO-CPA: Improving Compound-Protein Binding Affinity Prediction with Sequence Information and Gene Ontology Knowledge

SeqGO-CPA: Improving Compound-Protein Binding Affinity Predi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chunyu Wang Yan Zhu Naifeng Wen Lingling Zhao Junjie Wang Faculty of Computing Harbin Institute of Technology Harbin China School of Mechanical and Electrical Engineering Dalian Minzu University Dalian China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

ISBN: (纸本)9781665429825

The compound-protein binding affinity (CPA) pre-diction is vital for drug discovery and drug repurposing. Deep learning methods have been developed to model the complicated relationship between CPA and the sequences or structures of proteins and molecules. This study proposes a novel deep learning method, SeqGO-CPA, integrating protein function knowledge represented by Gene Ontology (GO) annotations in the CPA prediction. To capture the semantic information of GO annotations, a fine-tuned natural language processing model for biomedical domains is utilized to encode the set of GO terms. Meanwhile, based on the observation that CPA often occurs in sub-structures, our method uses the tokenization algorithm to learn sub-structure information of proteins and compounds from a large number of unlabeled sequences. Further, a deep neural network architecture involving the jointly-feature representation and a highway block is developed to enhance the CPA prediction ability. The proposed model was evaluated on two public benchmark datasets in both standard cross-validation and blinding split settings. The experimental results demonstrate our method outperforms the deep learning-based baselines, meanwhile the incorporating of GO information further improves the prediction performance.

关键词： Proteins Deep learning Drugs Annotations Biological system modeling Ontologies Prediction algorithms

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Network analysis reveals news press landscape and asymmetric user polarization

arXiv

引用

arXiv 2024年

作者： Lee, Byunghwee Ryu, Hyo-Sun Lee, Jae Kook Jeong, Hawoong Kim, Beom Jun Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Institute of Basic Science Sungkyunkwan University Suwon16419 Korea Republic of Department of Media and Communication Sungkyunkwan University Seoul03063 Korea Republic of Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Physics Sungkyunkwan University Suwon16419 Korea Republic of

Unlike traditional media, online news platforms allow users to consume content that suits their tastes and to facilitate interactions with other people. However, as more personalized consumption of information and interaction with like-minded users increase, ideological bias can inadvertently increase and contribute to the formation of echo chambers, reinforcing the polarization of opinions. Although the structural characteristics of polarization among different ideological groups in online spaces have been extensively studied, research into how these groups emotionally interact with each other has not been as thoroughly explored. From this perspective, we investigate both structural and affective polarization between news media user groups on Naver News, South Korea's largest online news portal, during the period of 2022 Korean presidential election. By utilizing the dataset comprising 333,014 articles and over 36 million user comments, we uncover two distinct groups of users characterized by opposing political leanings and re- veal significant bias and polarization among them. Additionally, we reveal the existence of echo chambers within co-commenting networks and investigate the asymmetric affective interaction patterns between the two polarized groups. Classification task of news media articles based on the distinct comment response patterns support the notion that different political groups may employ distinct communication strategies. Our approach based on network analysis on large-scale comment dataset offers novel insights into characteristics of user polarization in the online news platforms and the nuanced interaction nature between user groups. © 2024, CC BY.

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Predictive Power Consumption Model for Compute Intensive Applications in Clustered ARM A53 Embedded Systems

Predictive Power Consumption Model for Compute Intensive App...

引用

IEEE Latin American Symposium on Circuits and Systems (LASCAS)

作者： Jose Somarribas Esteban Meneses Kimberly Olivas School of Computing Costa Rica Institute of Technology School of Informatics Costa Rica National University

ISBN: (数字)9781728134277

ISBN: (纸本)9781728134284

High power consumption has been a concern in x86 architectures. In this same line, alternatives to x86 have been explored in order to have similar or higher ratio of computing capabilities with less power consumption. In order to find a power and cost efficient alternative for supercomputer architectures this paper explores the implementation of a low power ARM cluster based on embedded systems and analyses the cluster power consumption while running MiniMD, a compute intensive molecular dynamics workload. Based on MiniMD data, it is presented a predictive power consumption model for compute intensive applications with a 5% correlation error from real power measurements. The model also correlates within 3% error against Linpack measurements. Linpack is the compute intensive benchmark responsible for the "Top 500 supercomputers" ranking. Finally, by using the created model, power consumption projections for hypothetical cluster hardware configurations are presented. The projections exemplify how in the future, ARM based supercomputers will be a good alternative for reaching better power-performance capabilities.

关键词： Power demand Computational modeling Power measurement Predictive models Frequency measurement Hardware Mathematical model

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Learning Semantic-Agnostic and Spatial-Aware Representation for Generalizable Visual-Audio Navigation

arXiv

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arXiv 2023年

作者： Wang, Hongcheng Wang, Yuxuan Zhong, Fangwei Wu, Mingdong Zhang, Jianwei Wang, Yizhou Dong, Hao School of Computer Science Peking University Beijing100871 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China School of Intelligence and Technology Peking University National Key Laboratory of General Artificial Intelligence BIGAI Beijing100871 China TAMS Department of Informatics Universität Hamburg Germany The Center on Frontiers of Computing Studies Institute for Artificial Intelligence Peking University Beijing100871 China The Nat’l Eng. Research Center of Visual Technology Beijing100871 China

Visual-audio navigation (VAN) is attracting more and more attention from the robotic community due to its broad applications, e.g., household robots and rescue robots. In this task, an embodied agent must search for and navigate to the sound source with egocentric visual and audio observations. However, the existing methods are limited in two aspects: 1) poor generalization to unheard sound categories;2) sample inefficient in training. Focusing on these two problems, we propose a brain-inspired plug-and-play method to learn a semantic-agnostic and spatial-aware representation for generalizable visual-audio navigation. We meticulously design two auxiliary tasks for respectively accelerating learning representations with the above-desired characteristics. With these two auxiliary tasks, the agent learns a spatially-correlated representation of visual and audio inputs that can be applied to work on environments with novel sounds and maps. Experiment results on realistic 3D scenes (Replica and Matterport3D) demonstrate that our method achieves better generalization performance when zero-shot transferred to scenes with unseen maps and unheard sound categories. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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