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Using Weak Time-Delay and Transfer Learning to Improve Neural Network Performance in the Determination of Coagulant Dosing for Drinking Water Plants

SSRN

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SSRN 2023年

作者： Li, Si Zhu, Guocheng Fang, Haiquan Li, Xiaoshang Yuan, Cheng Yuan, Fang Wang, Jian Hursthouse, Andrew S. College of Civil Engineering Hunan University of Science and Technology Hunan Xiangtan411201 China Xiangtan Middle Ring Water Business Limited Corporation Hunan Xiangtan411201 China Tsinghua University Guangdong Provincial Engineering Technology Research Center Urban Water C Tsinghua Shenzhen Int Grad School Shenzhen518055 China Guangdong Provincial Engineering and Technology Research Center for Water Affairs Big Data a nd Water Ecology Shenzhen Water Planning & Design Institute Co. Ltd. Shenzhen518001 China School of Computing Engineering & Physical Sciences University of the West of Scotland PaisleyPA1 2BE United Kingdom

Coagulation is the most significant unit operation in drinking water treatment plants, which involves the precise control of coagulant dose, to minimize chemical expenditure. However, due to the significant time lag and external influences on the coagulation process, it can be difficult to build an appropriate coagulant dose prediction model. In this study, we proposed the concept of weak time delay and transfer learning to train the model and explain the sensitivity of factors affecting the prediction of coagulant dose, to improve the neural network model. The results showed that with the weak time-delay data including raw water and settling water quality parameters of one-hour delayed test, the model result improved with a more sensitive coagulant dose prediction. The sensitive factors included raw water turbidity, flow rate, temperature, total dissolved solids and settling water turbidity. Using these parameters, we were able to construct an effective model for prediction of coagulant dose, with a R2 correlation coefficient up to 0.72. The transfer learning technique successfully introduced the structure and parameters of the laboratory model for the training of the drinking water plant model. The results were consistent with the genetic algorithm, but its training speed increased by hundreds of times, and could also overcome the disadvantage of over fitting. Overall, the findings from this study provide new methods and technical support for the establishment of coagulation dose, neural network model. © 2023, The Authors. All rights reserved.

关键词： Time delay

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Learning How Spectator Reactions Affect Popularity on Twitch

Learning How Spectator Reactions Affect Popularity on Twitch

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International Conference on Big data and Smart computing (BIGCOMP)

作者： Jeongmin Kim Kunwoo Park Hyeonho Song Jaimie Y. Park Meeyoung Cha Hyperconnect Seoul Republic of Korea Qatar Computing Research Institute Doha Qatar School of Computing Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Data Science Group Institute for Basic Science Daejeon Republic of Korea Mobile Division Samsung Electronics Seoul Republic of Korea

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Gameplay live streaming has become a highly demanded online entertainment traffic on services like ***. The live chat concurrent with on-air streams allows viewers to talk about the content with each other. This new form of online content carries numerous spectator reactions, including text and emojis, and brings a unique opportunity to predict which video becomes popular in the end. This study presents a prediction model of viewer counts on a newly compiled dataset describing the video popularity of Twitch live streams and chat reactions of spectators. Our analysis demonstrates that the spectator reactions captured from the early-stage of live streams, such as the first 15 minutes out of several hours of content, hold essential markers of the eventual popularity of video streams. We discuss the implications of the findings and future directions.

关键词： Streaming media Games Predictive models Broadcasting Metadata Entertainment industry Big data

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Mental Health Consultations on College Campuses: Examining the Predictive Ability of Social Media

Research Square

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Research Square 2021年

作者： Saha, Koustuv Yousuf, Asra Boyd, Ryan L. Pennebaker, James W. De Choudhury, Munmun School of Interactive Computing Georgia Institute of Technology AtlantaGA United States Department of Psychology Lancaster University Lancaster United Kingdom Security Lancaster Lancaster University Lancaster United Kingdom Data Science Institute Lancaster University Lancaster United Kingdom Department of Psychology University of Texas at Austin AustinTX United States School of Interactive Computing Georgia Institute of Technology 756 W Peachtree St. NW AtlantaGA30308 United States

The mental health of college students is a growing concern, and gauging the mental health needs of college students is difficult to assess in real-time and in scale. While social media has shown potential as a viable "passive sensor" of mental health, the construct validity and in-practice reliability of such computational assessments remain largely unexplored. Towards this goal, we study how assessing the mental health of college students using social media data correspond with ground-truth data of on-campus mental health consultations. For a large U.S. public university, we obtained ground-truth data of on-campus mental health consultations between 2011–2016, and collected 66,000 posts from the university’s Reddit community. We adopted machine learning and natural language methodologies to measure symptomatic mental health expressions of depression, anxiety, stress, suicidal ideation, and psychosis on the social media data. Seasonal auto-regressive integrated moving average (SARIMA) models of forecasting on-campus mental health consultations showed that incorporating social media data led to predictions with r=0.86 and SMAPE=13.30, outperforming models without social media data by 41%. Our language analyses revealed that social media discussions during high mental health consultations months consisted of discussions on academics and career, whereas months of low mental health consultations saliently show expressions of positive affect, collective identity, and socialization. This study reveals that social media data can improve our understanding of college students’ mental health, particularly their mental health treatment needs. © 2021, CC BY.

关键词： Students

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Attention-Weighted Federated Deep Reinforcement Learning for Device-to-Device Assisted Heterogeneous Collaborative Edge Caching

Attention-Weighted Federated Deep Reinforcement Learning for...

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作者： Wang, Xiaofei Li, Ruibin Wang, Chenyang Li, Xiuhua Taleb, Tarik Leung, Victor C. M. College of Intelligence and Computing Tianjin University Tianjin China Ministry of Education China Department of Communications and Networking School of Electrical Engineering Aalto University Espoo Finland College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Big Data and Software Engineering Chongqing University Chongqing401331 China Department of Information Technology and Electrical Engineering Oulu University Oulu90570 Finland Department of Computer and Information Security Sejong University Seoul05006 Korea Republic of Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic. © 1983-2012 IEEE.

关键词： Reinforcement learning

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FSTR: Funds Skewness Aware Transaction Routing for Payment Channel Networks

FSTR: Funds Skewness Aware Transaction Routing for Payment C...

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International Conference on Dependable Systems and Networks (DSN)

作者： Siyi Lin Jingjing Zhang Weigang Wu School of Data and Computer Science Sun Yat-sen University Guangzhou China Guangdong Key Lab. of Big Data Analysis and Processing China MoE Key Lab. of Machine Intelligence and Advanced Computing China

ISBN: (数字)9781728158099

ISBN: (纸本)9781728158105

Payment channel is an effective and popular technique to improve the scalability and throughput of blockchains by transferring transactions from on-chain to off-chain. Multiple payment channels can constitute a payment network and realize transaction execution via multi-hop paths. How to find a feasible and efficient transaction path, i.e., transaction routing, is a key issue in payment channel networks, and different solutions have been proposed. However, the problem of funds skewness, which may cause routing failures, has been largely ignored in existing routing algorithms. In this work, we design FSTR, a routing algorithm that attempts to route transactions using a funds skewness based path selection scheme so as to reduce funds skewness and increase transaction success probability. To evaluate the performance of FSTR, we conduct experiments using the real-world dataset of Ripple. The experiment results show that FSTR outperforms existing routing algorithms, in terms of success ratio, delay, and overhead.

关键词： Routing Delays Throughput Scalability Spread spectrum communication Mice

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Embedding dynamic attributed networks by modeling the evolution processes

arXiv

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arXiv 2020年

作者： Xu, Zenan Ou, Zijing Su, Qinliang Yu, Jianxing Quan, Xiaojun Lin, Zhenkun School of Data and Computer Science Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Lab. of Machine Intelligence and Advanced Computing Ministry of Education China

Network embedding has recently emerged as a promising technique to embed nodes of a network into low-dimensional vectors. While fairly successful, most existing works focus on the embedding techniques for static networks. But in practice, there are many networks that are evolving over time and hence are dynamic, e.g., the social networks. To address this issue, a high-order spatio-temporal embedding model is developed to track the evolutions of dynamic networks. Specifically, an activeness-aware neighborhood embedding method is first proposed to extract the high-order neighborhood information at each given timestamp. Then, an embedding prediction framework is further developed to capture the temporal correlations, in which the attention mechanism is employed instead of recurrent neural networks (RNNs) for its efficiency in computing and flexibility in modeling. Extensive experiments are conducted on four real-world datasets from three different areas. It is shown that the proposed method outperforms all the baselines by a substantial margin for the tasks of dynamic link prediction and node classification, which demonstrates the effectiveness of the proposed methods on tracking the evolutions of dynamic networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Parallel branches-based second-order transformer for robust group re-identification with layout-guided occlusion mitigation

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Expert Systems with Applications 2025年 293卷

作者： Xu Zhang Zhiguang Wu Qinghua Zhang Zuyu Zhang Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing 400065 China Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism China Chongqing 400065 China School of Artificial Intelligence Chongqing University of Posts and Telecommunications Chongqing Chongqing 400065 China Dept. of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Chongqing 400065 China

Group Re-identification (GReID) seeks to accurately associate group images with the same members across different cameras, often used in expert and intelligent surveillance systems. However, existing methods mainly focus on the challenges of layout and membership variations while neglecting the occlusion problem caused by layout variations. To address this issue, we propose a novel Parallel Branches-based Second-Order Transformer (PB-SOT) framework, which consists of a Parallel Learning Branch (PLB) module and a Layout-guided Local Feature Sampling (LLFS) module. Specifically, the PLB module designs two group feature transformers with distinct weights to extract global and local features at the group level, respectively. The LLFS module leverages inter-member 2D layout to generate spatial density relationships and performs targeted sampling on local features with the highest density, mitigating the impact of occlusion with layout variations. Experimental evaluations conducted on three available datasets, including RoadGroup, CSG, and SYSU-Group, demonstrate the effectiveness and superiority of our proposed method. https://***/CQRhinoZ/PBSOT .

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Heat to Power: Thermal Energy Harvesting and Recycling for Warm Water-Cooled datacenters

Heat to Power: Thermal Energy Harvesting and Recycling for W...

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Annual International Symposium on Computer Architecture, ISCA

作者： Xinhui Zhu Weixiang Jiang Fangming Liu Qixia Zhang Li Pan Qiong Chen Ziyang Jia National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (数字)9781728146614

ISBN: (纸本)9781728146621

Warm water cooling has been regarded as a promising method to improve the energy efficiency of water-cooled datacenters. In warm water-cooling systems, hot spots occur as a common problem where the hybrid cooling architecture integrating thermoelectric coolers (TECs) emerges as a new remedy. Equipped with this architecture, the inlet water temperature can be raised higher, which provides more opportunities for heat recycling. However, currently, the heat absorbed from the server components is ejected directly into the water without being recycled, which leads to energy wasting. In order to further improve the energy efficiency, we propose Heat to Power (H2P), an economical and energy-recycling warm water cooling architecture, where thermoelectric generators (TEGs) harvest thermal energy from the “used” warm water and generate electricity for reusing in datacenters. Specifically, we propose some efficient optimization methods, including an economical water circulation design, fine-grained adjustments of the cooling setting and dynamic workload scheduling for increasing the power generated by TEGs. We evaluate H2P based on a real hardware prototype and cluster traces from Google and Alibaba. Experiment results show that TEGs equipped with our optimization methods can averagely generate 4.349 W, 4.203 W, and 3.979 W (4.177 W averagely) electricity on one CPU under the drastic, irregular and common workload traces, respectively. The power reusing efficiency (PRE) can reach 12.8%~16.2% (14.23% averagely) and the total cost of ownership (TCO) of datacenters can be reduced by up to 0.57%.

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Fuzzy Information Evolution with Three-Way Decision in Social Network Group Decision-Making

arXiv

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arXiv 2025年

作者： Jia, Qianlei Zhou, Xinliang Krejcar, Ondrej Herrera-Viedma, Enrique School of Automation Northwestern Polytechnical University 710072 China College of Computing and Data Science Nanyang Technological Univeristy 639798 Singapore FEEC VSB Technical University of Ostrava Ostrava708 00 Czech Republic Skoda Auto University Mlada Boleslav293 01 Czech Republic Faculty of Informatics and Management University of Hradec Kralove Hradec Kralove500 03 Czech Republic Andalusian Research Institute in Data Science and Computational Intelligence The Department of Computer Science and Artificial Intelligence University of Granada Granada18071 Spain

In group decision-making (GDM) scenarios, uncertainty, dynamic social structures, and vague information present major challenges for traditional opinion dynamics models. To address these issues, this study proposes a novel social network group decision-making (SNGDM) framework that integrates three-way decision (3WD) theory, dynamic network reconstruction, and linguistic opinion representation. First, the 3WD mechanism is introduced to explicitly model hesitation and ambiguity in agent judgments, thereby preventing irrational decisions. Second, a connection adjustment rule based on opinion similarity is developed, enabling agents to adaptively update their communication links and better reflect the evolving nature of social relationships. Third, linguistic terms are used to describe agent opinions, allowing the model to handle subjective, vague, or incomplete information more effectively. Finally, an integrated multi-agent decision-making framework is constructed, which simultaneously considers individual uncertainty, opinion evolution, and network dynamics. The proposed model is applied to a multi-UAV cooperative decision-making scenario, where simulation results and consensus analysis demonstrate its effectiveness. Experimental comparisons further verify the advantages of the algorithm in enhancing system stability and representing realistic decision-making behaviors. © 2025, CC0.

关键词： Risk perception

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Online clustering of streaming trajectories

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Frontiers of Computer science 2018年第2期12卷 245-263页

作者： Jiali MAO Qiuge SONG Cheqing JIN Zhigang ZHANG Aoying ZHOU School of Data Science and Engineering East China Normal University Shanghai 200062 China School of Computing China West Normal University Nanchong 637009 China

With the increasing availability of modern mobile devices and location acquisition technologies, massive trajectory data of moving objects are collected continuously in a streaming manner. Clustering streaming trajectories facilitates finding the representative paths or common moving trends shared by different objects in real time. Although data stream clustering has been studied extensively in the past decade, little effort has been devoted to dealing with streaming trajectories. The main challenge lies in the strict space and time complexities of processing the continuously arriving trajectory data, combined with the difficulty of concept drift. To address this issue, we present two novel synopsis structures to extract the clustering characteristics of trajectories, and develop an incremental algorithm for the online clustering of streaming trajectories （called OCluST）. It contains a micro-clustering component to cluster and summarize the most recent sets of trajectory line segments at each time instant, and a macro-clustering component to build large macro-clusters based on micro-clusters over a specified time horizon. Finally, we conduct extensive experiments on four real data sets to evaluate the effectiveness and efficiency of OCluST, and compare it with other congeneric algorithms. Experimental results show that OCluST can achieve superior performance in clustering streaming trajectories.

关键词： streaming trajectory synopsis data structure concept drift sliding window

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