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26th International Conference on Digital Audio Effects, DAFx 2023

作者： Gulli, Andrea Fontana, Federico Serafin, Stefania Geronazzo, Michele Department of Mathematics Computer Science and Physics Università di Udine Udine Italy Department of Architecture Design and Media Technology Aalborg University Copenhagen Denmark Department of Engineering and Management Università di Padova Padua Italy

Measuring the auditory lateralization elicited by interaural time difference (ITD) cues involves the estimation of a psychometric function (PF). The shape of this function usually follows from the analysis of the subjective data and models the probability of correctly localizing the angular position of a sound source. The present study describes and evaluates a procedure for progressively fitting a PF, using Gaussian process classification of the subjective responses produced during a binary decision experiment. The process refines adaptively an approximated PF, following Bayesian inference. At each trial, it suggests the most informative auditory stimulus for function refinement according to Bayesian active learning by disagreement (BALD) mutual information. In this paper, the procedure was modified to accommodate two-alternative forced choice (2AFC) experimental methods and then was compared with a standard adaptive "three-down, one-up" staircase procedure. Our process approximates the average threshold ITD 79.4% correct level of lateralization with a mean accuracy increase of 8.9% over the Weibull function fitted on the data of the same test. The final accuracy for the Just Noticeable Difference (JND) in ITD is achieved with only 37.6% of the trials needed by a standard lateralization test. © 2023 Andrea Gulli, Federico Fontana et al.

关键词： Inference engines

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Improvement and Research of Collaborative Filtering Algorithm Based on Penalty Factor

Improvement and Research of Collaborative Filtering Algorith...

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2021 International Conference on Electronic Communication, computer Science and technology, ECCST 2021

作者： Li, Panying Han, Yuxuan Wen, Xiumei Meng, Fanxing Computer Science and Technology Hebei Institute of Civil Engineering and Architecture Hebei Province Zhangjiakou075000 China Zhangjiakou Big Data Technology Innovation Center Hebei Province Zhangjiakou075000 China

The rapid development of the Internet has pushed society into the era of information explosion, and people are faced with more and more information screening and choices. A recommendation system is an effective way to process massive amounts of information, and it is also a tool that can make recommendations based on user behavior. Traditional collaborative filtering algorithms generally use the cosine similarity formula to calculate the similarity between users or items to make recommendations. Due to the popularity of the Internet, more and more popular items have appeared. The appearance of popular items not only affects the recommendation results, but also fails to reflect the real needs of users. This paper proposes an improved formula for cosine similarity with a penalty factor, which can restrain the influence of popular items on the recommendation result. Finally, the Movie Lens data set is used to verify that the recommended performance indicators have been improved to a certain extent. © Published under licence by IOP Publishing Ltd.

关键词： Collaborative filtering

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Data Availability Optimization for Cyber-Physical Systems 15

Data Availability Optimization for Cyber-Physical Systems

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2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green Computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Li, Liying Cong, Peijin Zhou, Junlong Gu, Zonghua Li, Keqin School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China Department of Applied Physics and Electronics Ume˚a University Sweden Department of Computer Science State University of New York United States

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

As the backbone of Industry 4.0, Cyber-Physical Systems (CPSs) have attracted extensive attention from industry, academia, and government. Missing data is a common problem in CPS data processing and may cause incorrect results and eventually serious malfunction. Existing data availability optimization methods either rely on a large amount of complete training data or suffer from poor performance. To solve these problems, this paper proposes an iterative data availability optimization method for CPSs. Specifically, the proposed method first pre-processes the raw dataset by using a Singular Value Decomposition-based feature selection approach to identify crucial features and reduce computation overheads. It then makes an initial guess for missing values via a designed K-Means-based imputation approach. The appropriate initial estimation decreases the probability of the proposed method falling into the local optimum. Finally, the proposed method iteratively estimates missing data based on the Orthogonal Matching Pursuit algorithm. The proposed method optimizes data availability by accurately imputing missing values. Simulation results on two datasets demonstrate that compared to multiple state-of-the-art approaches, the proposed data availability optimization method can reduce imputation error by up to 99.65%. © 2022 IEEE.

关键词： Iterative methods

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Modification of polyvinylidene fluoride membrane with ciprofloxacin to improve the bacteriostatic performance

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Chemosphere 2024年 363卷 142831页

作者： Wang, Zuxin Jiang, Suqi Lu, Yuhan Zhou, Haidong Yang, Zhiyun Deng, Jixin Pang, Lidan School of Environment and Architecture University of Shanghai for Science and Technology Shanghai200093 China School of Computer Engineering and Science Shanghai University Shanghai200444 China

The common polyvinylidene fluoride (PVDF) membrane itself is susceptible to membrane fouling, especially biofouling, which is a serious threat. In this study, PVDF membrane was modified with ciprofloxacin (CIP) through co-blending to investigate the filtration properties, bacterial inhibition and fouling resistance. Modified membranes were prepared by adding 0.3 g (MC0.3), 0.6 g (MC0.6), 0.9 g (MC0.9) and 1.2 g (MC1.2) CIP per 100 g casting solution. Among these modified membranes, MC0.6 showed the best filtration performances, with the pure water flux stabilized at about 416.67 L/(m2·h) and bovine serum albumin (BSA) rejection of 92.0% at a trans-membrane pressure of 0.1 MPa. The pore size was reduced, the average roughness was reduced to 29.4 nm, the contact angle was lowered to 68.9°, and the hydrophilicity was greatly improved. The width of the inhibition circle produced by MC0.6 was 0.35–0.45 mm, and the modified membrane showed good inhibition of non-specific bacteria and algal removal during urban river water filtration. The rejection of BSA was increased by 16.32% compared to the base membrane and the adsorption rate for BSA was reduced by 68.45%. In addition, the removal of conventional pollutants in urban river water by the modified membranes for was also improved. Compared with that of the base membrane, the removal of TN, NH3–N, TP and COD by MC0.6 was increased by 10.58%, 12.45%, 15.44% and 13.53%. The results showed that CIP co-blending modified PVDF membrane could effectively improve membrane performances and has good value for water treatment. © 2024 Elsevier Ltd

关键词： Membranes

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X-ray Prohibited Items Recognition Based on Improved YOLOv5 20th

X-ray Prohibited Items Recognition Based on Improved YOLOv5

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Proceedings of the 20th Web Information Systems and Applications Conference, WISA 2023

作者： Li, Wei Li, Xiang Liu, Wanxin Liu, Zhihan Jia, Jing Li, Jiayi College of Computer Science and Technology Harbin Engineering University Harbin150001 China Faculty of Arts Design and Architecture School of Built Environment The University of New South Wales SydneyNSW2052 Australia School of Art and Design Jiangsu Institute of Technology Changzhou213001 China

ISBN: (纸本)9789819962211

Safety inspections play a crucial role in maintaining social stability and protecting the safety of public life and property. The X-ray security detector is an important scanning device in the security inspection of public transportation and express packages. However, current intelligent detection algorithms face problems such as a limited dataset of prohibited items, the imbalanced distribution of categories, variable target postures, and varying target scales, leading to the occurrence of false positives and missed detections. We propose an improved X-ray prohibited item recognition algorithm, named YOLOv5s-DAB. A deformable convolution module is designed to address the characteristics of different scales and postures of the same prohibited item in different samples. Besides, a multi-scale feature enhancement module SA-ASPP based on attention mechanism is designed, which can handle the problem of overlapping occlusion of multi-scale contraband. Experimental results in the real X-ray prohibited items dataset demonstrate that our model outperforms state-of-the-art methods in terms of detection accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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FUCOM-Kendall Model for Evaluating E-Scooter Adoption: A Case Study in Catania, Italy

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Transportation Research Procedia 2025年 86卷 1-8页

作者： Vincenza Torrisi Rosalia Camporeale Sarbast Moslem Tiziana Campisi Department of Electric Electronic and Computer Engineering Catania Italy Department of Technology and Society Division of Transport and Roads Lund Sweden School of Architecture Planning and Environmental Policy University College Dublin Ireland Faculty of Engineering and Architecture University of Enna KORE Enna Italy

Electric scooters (e-scooters) have emerged as a fundamental component of urban micro-mobility, particularly in European cities during the Covid-19 pandemic. This study employs the Full Consistency Method (FUCOM) and Kendall rank correlation method to assess the preferences and priorities of university students in Catania, Italy, regarding e-scooter adoption. By analyzing influencing factors, this research offers a comprehensive understanding of student attitudes towards e-scooters and their adoption, contributing to strategies for enhancing sustainable urban mobility. The findings highlight significant preferences for factors such as parking ease, portability, and intermodality, while also identifying safety concerns and the need for better integration with public transport. These insights underscore the necessity for targeted improvements in e-scooter infrastructure and policies, aiming to optimize urban mobility and meet the specific needs of potential users. Further research will focus on comparative analysis across different urban settings, investigating addition user-centric factors to capture a more comprehensive view of e-scooter adoption.

关键词： e-scooter multi-criteria decision making FUCOM method Kendall rank correlation urban mobility

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Prediction of cognitive scores by joint use of movie-watching fMRI connectivity and eye tracking via Attention-CensNet

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Psychoradiology 2023年第1期3卷 193-202页

作者： Jiaxing Gao Lin Zhao Tianyang Zhong Changhe Li Zhibin He Yaonei Wei Shu Zhang Lei Guo Tianming Liu Junwei Han Xi Jiang Tuo Zhang School of Automation Northwestern Polytechnical UniversityXIi'an 710072China Cortical Architecture Imaging and Discovery Laboratory Department of Computer Science and Bioimaging Research CenterThe University of GeorgiaAthensGA 30602USA School of Computer Science Northwestern Polytechnical UniversityXi'an 710072China School of Life Science and Technology MOE Key Laboratory for NeuroinformationUniversity of Electronic Science and Technology of ChinaChengdu 611731China

Background:Brain functional connectivity under the naturalistic paradigm has been shown to be better at predicting individual behaviors than other brain states,such as rest and doing ***,the state-of-the-art methods have found it difcult to achieve desirable results from movie watching paradigm functional magnetic resonance imaging(mfMRI)-induced brain functional connectivity,especially when there are fewer *** other physical measurements into the prediction method may enhance *** tracking,becoming popular due to its portability and lower expense,can provide abundant behavioral features related to the output of human's cognition,and thus might supplement the mfMRI in observing participants'subconscious ***,there are very few studies on how to effectively integrate the multimodal information to strengthen the performance by a unified ***:A fusion approach with mfMRI and eye tracking,based on convolution with edge-node switching in graph neural networks(CensNet),is proposed in this ***:In this graph model,participants are designated as nodes,mfMRI derived functional connectivity as node features,and different eye-tracking features are used to compute similarity between participants to construct heterogeneous graph *** taking multiple graphs as different channels,we introduce squeeze-and-excitation attention module to CensNet(A-CensNet)to integrate graph embeddings from multiple channels into ***:The proposed model outperforms those using a single modality and single channel,and state-of-the-art ***:The results indicate that brain functional activities and eye behaviors might complement each other in interpreting trait-like phenotypes.

关键词： functional connectivity naturalistic stimulus eye movement CensNet attention

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SEER: A Time Prediction Model for CNNs from GPU Kernel's View 21

SEER: A Time Prediction Model for CNNs from GPU Kernel's Vie...

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Proceedings of the 30th International Conference on Parallel architectures and Compilation Techniques

作者： Guodong Liu Sa Wang Yungang Bao State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China and University of Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China and Institute of Computing Technology (Nanjing) Chinese Academy of Sciences Nanjing China State Key Laboratory of Computer Architecture Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China and Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9781665442787

With the deepening of research and increasing size of data sets, deep neural networks have become larger and larger. To reduce the training time of large neural networks, researchers propose to optimize neural networks from different levels. When performing optimizations, prior knowledge about execution time of each part of the network can help avoid repeatedly time-consuming testing and profiling process. However it is quite challenging to build an accurate iteration time prediction model, due to opaque underlying implementation of network operators and complex architecture of accelerators. In this paper, we propose SEER, an iteration time prediction model for CNNs, targeting on GPU platforms. We propose to categorize convolution kernels into three different types: Compute-bound, DRAM-bound and Under-utilized, then we build performance model for each type respectively. We combined analytical models and learning-based models to make the performance model accurate and in line with GPU execution model. Experimental results show that, our model achieves 14.71% prediction error on convolution kernels and up to 1.79% prediction error for the overall computation time in one iteration of common CNNs. Besides, when used for selecting the best convolution algorithm, our model shows 7.14% lower error rate than cuDNN's official algorithm picker.

关键词： GPU

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The Mechanism for Synchronizing CNC System with External Devices by Using a Part Program

The Mechanism for Synchronizing CNC System with External Dev...

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International Russian Automation Conference (RusAutoCon)

作者： Georgi Martinov Evgeny Salamatin Department of Computer-Architecture Control Systems Moscow State University of Technology "STANKIN" Moscow Russia

The problem of synchronizing parallel tasks in control systems at the level of the part program is reviewed. A general solution, based on a high-level part programming language extension, is proposed for synchronizing parallel tasks within one control system as well as tasks performed by several different control systems. A scheme for synchronizing the CNC with external autonomous devices through the Common PLC Memory, to which the part program, the PLC program and the inputs and outputs (I/Os) of electric automation have access, has been developed. A set of functions in a high-level part programming language, providing zeroing a certain number of bytes, writing bit, byte, long and float data types to the Common PLC Memory and reading them from it, has been implemented. As a part of practical research, a test stand on the basis of the AxiOMA Control CNC system, that controls actuators via the EtherCAT protocol, was assembled; master and slave part programs were implemented; tests, confirming the operability of the mechanism for synchronizing CNC system with external devices by using a part program, were carried out.

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Collection and Storage of Technological Data in Multi-Channel CNC Processing Using a Non-Relational Database and Node-RED

Collection and Storage of Technological Data in Multi-Channe...

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International Russian Automation Conference (RusAutoCon)

作者： Georgi Martinov Kirill Polyakov Department of Computer-Architecture Control Systems Moscow State University of Technology "STANKIN" Moscow Russia

This article contemplates the problem of collecting and storing technological data during multichannel and multi - coordinate machining on CNC machines obtained using the OPC UA protocol and applying Node-RED open-source instrumentation. A structural model of obtaining, visualizing and storing technological data in multichannel and multi - coordinate machining is proposed in this research. The necessary nodes for Collection and storage of technological data in multi-channel CNC processing are implemented and configured. The researchers illustrated practical aspects of building a system for collecting and storing technological data in multichannel machining using the OPC UA protocol and applying Node-RED open-source tools and a non-relational MongoDB database.

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