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Correction: An explainable AI based new deep learning solution for efficient heart disease prediction at early stages

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Evolving Systems 2025年第2期16卷 1-1页

作者： Ashfaq, Muhammad Talha Javaid, Nadeem Alrajeh, Nabil Ali, Syed Saqib Department of Computer Science COMSATS University Islamabad Islamabad Pakistan ComSens Lab International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Yunlin Taiwan Department of Biomedical Technology College of Applied Medical Sciences King Saud University Riyadh Saudi Arabia Department of Computer Science and Engineering Soongsil University Seoul South Korea

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Efficient compilation of lazy evaluation

Efficient compilation of lazy evaluation

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作者： Johnsson, Thomas Programming Methodology Group Department of Computer Science Chalmers University of Technology S-412 96 Göteborg Sweden

This paper describes the principles underlying an efficient implementation of a lazy functional language, compiling to code for ordinary computers. It is based on combinator-like graph reduction: the user defined functions are used as rewrite rules in the graph. Each function is compiled into an instruction sequence for an abstract graph reduction machine, called the G-machine, the code reduces a function application graph to its value. The G-machine instructions are then translated into target code. Speed improvements by almost two orders of magnitude over previous lazy evaluators have been measured;we provide some performance figures.

关键词： computer programming languages

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A novel multi-modal tactile sensor design using thermochromic material

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science China(Information sciences) 2019年第11期62卷 189-191页

作者： Fuchun SUN Bin FANG Hongxiang XUE Huaping LIU Haiming HUANG Department of Computer Science and Technology Tsinghua University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University College of Information Engineering Shenzhen University

Tactile sensors are important for ensuring robotic perception [1], and their performance directly affects a robot’s capability for performing dexterous operations. These sensors mimic the tactile sensing capability of human hands. This is because the skin of human hands is involved in considerably meticulous works and can provide multi-

关键词： A novel multi-modal tactile sensor design using thermochromic material

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Correlations between characteristics of maximum influence and degree distributions in software networks

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science China(Information sciences) 2014年第7期57卷 25-36页

作者： GU Qing XIONG ShiJie CHEN DaoXu National Key Lab of Novel Software Technology and Department of Computer Science and Technology Nanjing University National Laboratory of Solid State Microstructures and Department of Physics Nanjing University

Software systems can be represented as complex networks and their artificial nature can be investigated with approaches developed in network *** maximization has been successfully applied on software networks to identify the important nodes that have the maximum influence on the other ***,research is open to study the effects of network fabric on the influence behavior of the highly influential *** this paper,we construct class dependence graph(CDG)networks based on eight practical Java software systems,and apply the procedure of influence maximization to study empirically the correlations between the characteristics of maximum influence and the degree distributions in the software *** demonstrate that the artificial nature of CDG networks is reflected partly from the scale free behavior:the in-degree distribution follows power law,and the out-degree distribution is *** the influence behavior,the expected influence spread of the maximum influence set identified by the greedy method correlates significantly with the degree *** addition,the identified influence set contains influential classes that are complex in both the number of methods and the lines of code(LOC).For the applications in software engineering,the results provide possibilities of new approaches in designing optimization procedures of software systems.

关键词： software network scale free influence maximization power law complex network

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Lazy memo-functions 2nd

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2nd International Conference on Functional programming Languages and computer Architecture, 1985

作者： Hughes, John Programming Methodology Group Department of Computer Science Chalmers University of Technology GöteborgS-412 96 Sweden

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Mobile Internet Big Data Platform in China Unicom

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Tsinghua science and technology 2014年第1期19卷 95-101页

作者： Wenliang Huang Zhen Chen Wenyu Dong Hang Li Bin Cao Junwei Cao China Unicom Groups No. 21 Financial Street Xicheng District Research Institute of Information Technology (RIIT) and Tsinghua National Lab for Information Science and Technology (TNList) Tsinghua University Department of Computer Science and Technology Tsinghua University Department of Computer Science and Technology PLA Univ. of Info. & Eng.

China Unicorn, the largest WCDMA 3G operator in China, meets the requirements of the historical Mobile Internet Explosion, or the surging of Mobile Internet Traffic from mobile terminals. According to the internal statistics of China Unicom, mobile user traffic has increased rapidly with a Compound Annual Growth Rate （CAGR） of 135%. Currently China Unicorn monthly stores more than 2 trillion records, data volume is over 525 TB, and the highest data volume has reached a peak of 5 PB. Since October 2009, China Unicom has been developing a home-brewed big data storage and analysis platform based on the open source Hadoop Distributed File System （HDFS） as it has a long-term strategy to make full use of this Big Data. All Mobile Internet Traffic is well served using this big data platform. Currently, the writing speed has reached 1 390 000 records per second, and the record retrieval time in the table that contains trillions of records is less than 100 ms. To take advantage of this opportunity to be a Big Data Operator, China Unicom has developed new functions and has multiple innovations to solve space and time constraint challenges presented in data processing. In this paper, we will introduce our big data platform in detail. Based on this big data platform, China Unicom is building an industry ecosystem based on Mobile Internet Big Data, and considers that a telecom operator centric ecosystem can be formed that is critical to reach prosperity in the modern communications business.

关键词： big data platform China Unicorn 3G wireless network Hadoop Distributed File System （HDFS） mobilenternet network forensic data warehouse HBase

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Rendering discrete participating media using geometrical optics approximation

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Computational Visual Media 2022年第3期8卷 425-444页

作者： Jie Guo Bingyang Hu Yanjun Chen Yuanqi Li Yanwen Guo Ling-Qi Yan State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China Department of Computer Science UC Santa BarbaraUSA

We consider the scattering of light in participating media composed of sparsely and randomly distributed discrete *** particle size is expected to range from the scale of the wavelength to several orders of magnitude greater,resulting in an appearance with distinct graininess as opposed to the smooth appearance of continuous *** fundamental issue in the physically-based synthesis of such appearance is to determine the necessary optical properties in every local *** these properties vary spatially,we resort to geometrical optics approximation(GOA),a highly efficient alternative to rigorous Lorenz–Mie theory,to quantitatively represent the scattering of a single *** enables us to quickly compute bulk optical properties for any particle size *** then use a practical Monte Carlo rendering solution to solve energy transfer in the discrete participating *** proposed framework is the first to simulate a wide range of discrete participating media with different levels of graininess,converging to the continuous media case as the particle concentration increases.

关键词： light scattering geometrical optics approximation(GOA) discrete participating media volume rendering

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Graph-Tensor FISTA-Net: Edge Computing-Aided Deep Learning for Distributed Traffic Data Recovery

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IEEE Transactions on Network science and Engineering 2025年

作者： Deng, Lei Liu, Xiao-Yang Zheng, Haifeng Feng, Xinxin Zhu, Ming Tsang, Danny H. K. Fuzhou University Fujian Key Lab for Intelligent Processing and Wireless Transmission of Media Information College of Physics and Information Engineering Fuzhou China Internet of Things Thrust Guangdong Guangzhou511400 China Columbia University Department of Electrical Engineering NY United States Chinese Academy of Sciences Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay Hong Kong

In intelligent transportation systems, deep learning is a widely adopted technique for traffic data recovery. In city-wide traffic data recovery tasks, traditional centralized deep-learning-model training strategies become inapplicable because of the expensive storage costs for large-scale traffic datasets. In this scenario, edge computing emerges as a natural choice, allowing decentralized data storage and distributed training on edge nodes. However, there is still a challenge: distributed training on edge nodes suffers from high communication costs for parameter transmission. In this paper, we propose a communication-efficient Graph-Tensor Fast Iterative Shrinkage-Thresholding Algorithm-based neural Network (GT-FISTA-Net) for distributed traffic data recovery. Firstly, we model the recovery task as a graph-tensor completion problem to better capture the low-rankness of traffic data. A recovery guarantee is also provided to characterize the performance bounds of the proposed scheme in terms of recovery error. Secondly, we propose a distributed graph-tensor completion algorithm and unfold it into a deep neural network called GT-FISTA-Net. GT-FISTA-Net requires small communication costs for distributed model training on edge nodes and thus it is applicable for city-wide traffic data recovery. Extensive experiments on real-world datasets show that the proposed GT-FISTA-Net can also provide excellent recovery accuracy compared with state-of-the-art distributed recovery methods. © 2013 IEEE.

关键词： distributed learning Edge computing Graph-tensor tensor completion traffic data recovery

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DPNet: Dynamic Pooling Network for Accurate and Efficient Size-Aware Tiny Object Detection

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IEEE Internet of Things Journal 2025年

作者： Gong, Luqi Chen, Haotian Chen, Yikun Yao, Tianliang Li, Chao Zhao, Shuai Han, Guangjie Beijing University of Posts and Telecommunications School of Computer Science Beijing China Zhejiang Lab Research Center for Space Computing System Hangzhou China Southwest Jiaotong University SWJTU-LEEDS Joint School Chengdu China LTD Guangdong Zhiyun City construction Technology Co Zhuhai China Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

In unmanned aerial systems, especially in complex environments, accurately detecting tiny objects is crucial. Resizing images is a common strategy to improve detection accuracy, particularly for small objects. However, simply enlarging images significantly increases computational costs and the number of negative samples, severely degrading detection performance and limiting its applicability. This paper proposes a Dynamic Pooling Network (DPNet) for tiny object detection to mitigate these issues. DPNet employs a flexible down-sampling strategy by introducing a factor (df) to relax the fixed down-sampling process of the feature map to an adjustable one. Furthermore, we design a lightweight predictor to predict df for each input image, which will be used to decrease the resolution of feature map in backbone. Thus, we achieve input-aware down-sampling. We design an Adaptive Normalization Module (ANM) to make a unified detector well compatible with different dfs. At the same time, we also design a guidance loss to supervise the predictor’s training. DPNet realizes the dynamic allocation of computing resources to trade off detection accuracy and efficiency through this. Experiments on the TinyCOCO and TinyPerson datasets show that our DPNet can save over 35% and 25% GFLOPs, respectively, while maintaining comparable detection *** code will be made publicly available. © 2014 IEEE.

关键词： Object detection

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Automatic Classification of Strain in the Singing Voice Using Machine Learning

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Journal of Voice 2025年

作者： Liu, Yuanyuan Reddy, Mittapalle Kiran Yagnavajjula, Madhu Keerthana Räsänen, Okko Alku, Paavo Ikävalko, Tero Hakanpää, Tua Öyry, Aleksi Laukkanen, Anne-Maria Speech and Voice Research Laboratory Tampere University Tampere 33100 Finland Department of Computer Science and Engineering Indian Institute of Information Technology Raichur Karanataka Raichur 584135 India Advanced Technology Development Centre Indian Institute of Technology Kharagpur West Bengal Kharagpur 721302 India Unit of Computing Sciences Tampere University Tampere 33720 Finland Department of Information and Communications Engineering Aalto University Espoo 02150 Finland Faculty of Education University of Turku Turku 20014 Finland Aalto Acoustics Lab Aalto University Espoo 02150 Finland

Objectives: Classifying strain in the singing voice can help protect professional singers from vocal overuse and support singing training. This study investigates whether machine learning can automatically classify singing voices into two levels of perceived strain. The singing samples represent two genres: classical and contemporary commercial music (CCM). Methods: A total of 324 singing voice samples from 15 professional normophonic singers (nine female, six male) were analyzed. Nine singers were classical, and six were CCM singers. The samples consisted of syllable strings produced at three to six pitches and three loudness levels. Based on expert auditory-perceptual ratings, the samples were categorized into two strain levels: normal-mild and moderate-severe. Three acoustic feature sets (mel-frequency cepstral coefficients (MFCCs), the extended Geneva Minimalistic Acoustic Parameter Set (eGeMAPS), and wavelet scattering features) were compared using two classifier models [support vector machine (SVM) and multilayer perceptron (MLP)]. Feature selection was performed using recursive feature elimination, and the Mann-Whitney U test was used to assess the discriminative power of the selected features. Results: The highest classification accuracy of 86.1% was achieved using a subset of wavelet scattering features with the MLP classifier. A comparison between individual features showed that the first MFCC coefficient, representing spectral tilt, exhibited the greatest between-class separation. Conclusion: This study demonstrates that machine learning models utilizing selected acoustic features can classify perceptual strain of singing voices automatically with high accuracy. These preliminary findings highlight the potential for larger studies involving more diverse singer groups across different genres. © 2025 The Authors

关键词： Auditive-perceptual evaluation—Support vector machine—Multiple layer perceptron—Fisher vector—Wavelet scattering coefficients—Mel-frequency cepstral coefficients

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