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Time series forecast of sales volume based on XGBoost

Journal of Physics: Conference Series 2021年第1期1873卷

作者： Lingyu Zhang Wenjie Bian Wenyi Qu Liheng Tuo Yunhai Wang School of Computer Science and Technology Shandong University Qingdao China Didi AI Labs Didi Chuxing Beijing China Smart Transportation Center National Engineering Laboratory for Big Data Analysis and Applications Beijing China

Some problems such as the decline of new labor force, the increase of retired labor force emerge because of the complex and changeable market environment, consequently exacerbating the staffing problem in the retail industry. Also, the unreasonable distribution of personnel, there are few people in busy hours and too many people in idle hours, which causes waste of labor. To address this issue, we analyzed the time series of sales volume in the retail industry in detail, and processed the data with feature engineering for predicting the in-store sales volume in the future. At the same time, other features such as weather and temperature are added to improve the accuracy of the model. Considering the characteristics of the data, we choose XGBoost as the prediction model. The experiments on real-world datasets verified better performance of proposed model compared with other state-of-art models.

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DRL360: 360-degree Video Streaming with Deep Reinforcement Learning

DRL360: 360-degree Video Streaming with Deep Reinforcement L...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yuanxing Zhang Pengyu Zhao Kaigui Bian Yunxin Liu Lingyang Song Xiaoming Li School of Electronics Engineering and Computer Science Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Beijing China Microsoft Research Beijing China

360-degree videos have gained more popularity in recent years, owing to the great advance of panoramic cameras and head-mounted devices. However, as 360-degree videos are usually in high resolution, transmitting the content requires extremely high bandwidth. To protect the Quality of Experience (QoE) of users, researchers have proposed tile-based 360-degree video streaming systems that allocate high/low bit rates to selected tiles of video frames for streaming over the limited bandwidth. It is challenging to determine which tiles should be allocated with a high/low rate, because (1) the video playbacks include too many features that dynamically change over time when making the rate allocation; (2) most of the state-of-the-art systems focus on a fixed set of heuristics to optimize a specific QoE objective, while users may have various QoE objectives that need to be optimized in different ways. This paper presents a Deep Reinforcement Learning (DRL) based framework for 360-degree video streaming, named DRL360. The DRL360 framework helps improve the system performance by jointly optimizing multiple QoE objectives across a broad set of dynamic features. The DRL-based model adaptively allocates rates for the tiles of the future video frames based on the observations collected by client video players. We compare the proposed DRL360 to the existing systems by trace-driven evaluations as well as conducting a realworld experiment over a wide variety of network conditions. Evaluation results reveal that DRL360 can adapt to all considered scenarios, and outperform the state-of-the-art approaches by 20%-30% on average given different QoE objectives.

关键词： Streaming media Quality of experience Resource management Bandwidth Adaptation models Reinforcement learning Bit rate

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Radiomics based on dual-energy CT for noninvasive prediction of cervical lymph node metastases in patients with nasopharyngeal carcinoma

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Radiography 2025年第4期31卷 102989-102989页

作者： Li, L. Yang, D. Wu, Y. Sun, R. Qin, Y. Kang, M. Deng, X. Bu, M. Li, Z. Zeng, Z. Zeng, X. Jiang, M. Chen, B.T. Department of Radiology First Affiliated Hospital of Guangxi Medical University Guangxi Nanning 530021 China Department of Radiology Guizhou Provincial People Hospital No.83 East Zhongshan Road Nanming District Guizhou Province Guiyang 550000 China Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University No. 2870 Huaxi Avenue South Guizhou Guiyang 550025 China Department of Nuclear Medicine First Affiliated Hospital of Guangxi Medical University Guangxi Nanning 530021 China Department of Radiation Oncology First Affiliated Hospital of Guangxi Medical University Guangxi Nanning 530021 China Department of Diagnostic Radiology City of Hope National Medical Center Duarte CA United States

Introduction: To develop and validate a machine learning model based on dual-energy computed tomography (DECT) for predicting cervical lymph node metastases (CLNM) in patients diagnosed with nasopharyngeal carcinoma (NPC). Methods: This prospective single-center study enrolled patients with NPC and the study assessment included both DECT and 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). Radiomics features were extracted from each region of interest (ROI) for cervical lymph nodes using arterial and venous phase images at 100 keV and 150 keV, either individually as non-fusion models or combined as fusion models on the DECT images. The performance of the random forest (RF) models, combined with radiomics features, was evaluated by area under the receiver operating characteristic curve (AUC) analysis. DeLong's test was employed to compare model performances, while decision curve analysis (DCA) assessed the clinical utility of the predictive models. Results: Sixty-six patients with NPC were included for analysis, which was divided into a training set (n = 42) and a validation set (n = 22). A total of 13 radiomic models were constructed (4 non-fusion models and 9 fusion models). In the non-fusion models, when the threshold value exceeded 0.4, the venous phase at 100 keV (V100) (AUC, 0.9667;95 % confidence interval [95 % CI], 0.9363–0.9901) model exhibited a higher net benefit than other non-fusion models. The V100 + V150 fusion model achieved the best performance, with an AUC of 0.9697 (95 % CI, 0.9393–0.9907). Conclusion: DECT-based radiomics effectively diagnosed CLNM in patients with NPC and may potentially be a valuable tool for clinical decision-making. Implications for practice: This study improved pre-operative evaluation, treatment strategy selection, and prognostic evaluation for patients with nasopharyngeal carcinoma by combining DECT and radiomics to predict cervical lymph node status prior to treatment. © 2025 The Co

关键词： Cervical lymph node metastases (CLNM) Dual-energy computed tomography (DECT) Machine learning model Nasopharyngeal carcinoma (NPC) Radiomics

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Abnormal Traffic Detection Method Based on DCNN-GRU Architecture in SDN

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International Journal of Intelligent Systems 2025年第1期2025卷

作者： Xueyuan Duan Kun Wang Yu Fu Taotao Liu Yihan Yu Jianqiao Xu Lu Wang College of Computer and Information Technology Xinyang Normal University Xinyang 464000 Henan China *** Department of Information Security Naval University of Engineering Wuhan 430033 Hubei China *** Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Xinyang 464000 China *** School of Information and Communication Engineering Xinyang Vocational and Technical College Xinyang 464000 Henan China *** Department of Operational Research and Programming Naval University of Engineering Wuhan 430033 China *** Department of Economic Crime Investigation Henan Police College Zhengzhou 450046 Henan China

In response to the centralized single-architecture abnormal traffic detection method in Software Defined Network (SDN), which consumes massive computational and network resources, and may lead to the decline of service quality of SDN network, this paper proposes a large-scale abnormal traffic detection method of SDN network based on Distributed Convolutional Neural Networks and Gate Recurrent Unit (DCNN-GRU) architecture. This method utilizes lightweight detection agents based on CNN deployed on each controller to extract traffic features preliminarily. Then it inputs the feature data into the GRU-based deep detection model hosted in the cloud for collaborative training and completes the final abnormal detection task. Since the feature extraction tasks are distributed across multiple controllers, the cloud server only needs to relearn and classify the extracted feature data, which is less costly than directly extracting feature information from the original traffic data and occupies less bandwidth resources than transmitting complete data packets. The experiment shows that the method achieves an abnormal detection accuracy of 0.9939, a recall rate of 0.9831, and a false alarm rate of only 0.0244, obtaining a higher precision and lower false alarm rate than traditional detection methods.

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Model-assisted inference for covariate-specific treatment effects with high-dimensional data

arXiv

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arXiv 2021年

作者： Wu, Peng Tan, Zhiqiang Hu, Wenjie Zhou, Xiao-Hua Beijing International Center for Mathematical Research Peking University Beijing100871 China Department of Statistics Rutgers University 110 Frelinghuysen Road PiscatawayNJ08854 United States Department of Probability and Statistics Peking University Beijing100871 China Department of Biostatistics Beijing International Center for Mathematical Research National Engineering Laboratory of Big Data Analysis and Applied Technology Peking University Beijing100871 China

Covariate-specific treatment effects (CSTEs) represent heterogeneous treatment effects across subpopulations defined by certain selected covariates. In this article, we consider marginal structural models where CSTEs are linearly represented using a set of basis functions of the selected covariates. We develop a new approach in high-dimensional settings to obtain not only doubly robust point estimators of CSTEs, but also model-assisted confidence intervals, which are valid when a propensity score model is correctly specified but an outcome regression model may be misspecified. With a linear outcome model and subpopulations defined by discrete covariates, both point estimators and confidence intervals are doubly robust for CSTEs. In contrast, confidence intervals from existing high-dimensional methods are valid only when both the propensity score and outcome models are correctly specified. We establish asymptotic properties of the proposed point estimators and the associated confidence intervals. We present simulation studies and empirical applications which demonstrate the advantages of the proposed method compared with competing ones. Copyright © 2021, The Authors. All rights reserved.

关键词： Clustering algorithms

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The wisdom of crowds versus the madness of mobs: An evolutionary model of bias, polarization, and other challenges to collective intelligence

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Collective Intelligence 2022年第1期1卷 26339137221104785页

作者： Andrew W Lo Ruixun Zhang MIT Laboratory for Financial Engineering Cambridge MA USA MIT Sloan School of Management Cambridge MA USA MIT Computer Science and Artificial Intelligence Laboratory Cambridge MA USA Santa Fe Institute Santa Fe NM USA School of Mathematical Sciences Peking University Beijing China Center for Statistical Science Peking University Beijing China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China

Despite its success in financial markets and other domains, collective intelligence seems to fall short in many critical contexts, including infrequent but repeated financial crises, political polarization and deadlock, and various forms of bias and discrimination. We propose an evolutionary framework that provides fundamental insights into the role of heterogeneity and feedback loops in contributing to failures of collective intelligence. The framework is based on a binary choice model of behavior that affects fitness; hence, behavior is shaped by evolutionary dynamics and stochastic changes in environmental conditions. We derive collective intelligence as an emergent property of evolution in this framework, and also specify conditions under which it fails. We find that political polarization emerges in stochastic environments with reproductive risks that are correlated across individuals. Bias and discrimination emerge when individuals incorrectly attribute random adverse events to observable features that may have nothing to do with those events. In addition, path dependence and negative feedback in evolution may lead to even stronger biases and levels of discrimination, which are locally evolutionarily stable strategies. These results suggest potential policy interventions to prevent such failures by nudging the “madness of mobs” towards the “wisdom of crowds” through targeted shifts in the environment.

关键词： Collective intelligence political polarization bias discrimination evolutionarily stable strategy group selection

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R2-Net: Relation of relation learning network for sentence semantic matching

arXiv

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arXiv 2020年

作者： Zhang, Kun Wu, Le Lv, Guangyi Wang, Meng Chen, Enhong Ruan, Shulan School of Computer Science and Information Engineering Hefei University of Technology China Institute of Artificial Intelligence Hefei Comprehensive National Science Center China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China China

Sentence semantic matching is one of the fundamental tasks in natural language processing, which requires an agent to determine the semantic relation among input sentences. Recently, deep neural networks have achieved impressive performance in this area, especially BERT. Despite their effectiveness, most of these models treat output labels as meaningless one-hot vectors, underestimating the semantic information and guidance of relations that these labels reveal, especially for tasks with a small number of labels. To address this problem, we propose a Relation of Relation Learning Network (R2-Net) for sentence semantic matching. Specifically, we first employ BERT to encode the input sentences from a global perspective. Then a CNN-based encoder is designed to capture keywords and phrase information from a local perspective. To fully leverage labels for better relation information extraction, we introduce a self-supervised relation of relation classification task for guiding R2-Net to consider more about relations. Meanwhile, a triplet loss is employed to distinguish the intra-class and inter-class relations in a finer granularity. Empirical experiments on two sentence semantic matching tasks demonstrate the superiority of our proposed model. As a byproduct, we have released the codes to facilitate other researches. © 2020, CC BY.

关键词： Natural language processing systems

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Improving The Biodegradability of Rice Straw by Electrochemical Pretreatment

SSRN

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SSRN 2022年

作者： Sun, Shaohua Zhang, Yuanyuan Yang, Zhaoyang Liu, Chunmei Zuo, Xiaoyu Tang, Yang Wan, Pingyu Liu, Yanping Li, Xiujin Coulon, Frederic Hu, Qing Yang, Xiao Jin State Key Laboratory of Chemical Resource Engineering Beijing University of Chemical Technology 15 Beisanhuan East Road Chaoyang District Beijing100029 China Beijing City Environmental Pollution Control and Resource Reuse Engineering Center Beijing University of Chemical Technology 15 Beisanhuan East Road Chaoyang District Beijing100029 China National Fundamental Research Laboratory of New Hazardous Chemicals Assessment and Accident Analysis Beijing University of Chemical Technology Beijing100029 China School of Water Energy and Environment Cranfield University Cranfield MK43 0AL United Kingdom School of Environmental Science and Engineering South University of Science and Technology Guangdong Shenzhen518055 China Beijing Huanding Environmental Big Data Institute No. 1 Wangzhuang Road Beijing100083 China

In this study, the efficacy of three different pretreatments of rice straw including alkaline hydrogen peroxide (NaOH-H2O2), electrochemically produced NaOH-H2O2 (eNaOH-H2O2) and electrohydrolysis (EH) was investigated. All three pretreatments allowed to reach 80% of total biogas production within 23 and 25 days which was 3 to 5 days faster compared to conventional NaOH pretreatment. The cumulative biogas production was enhanced by 8%, 22% and 14% for NaOH-H2O2, eNaOH-H2O2 and EH, respectively compared to traditional treatment. The eNaOH-H2O2 pretreatment decreased the crystallinity of cellulose and created a porous surface vein, thus promoting microbial degradation of lignocellulose. The findings of this study demonstrate that electrochemical pretreatment provides a promising approach for efficiently utilizing rice straw biomass to produce renewable electricity. © 2022, The Authors. All rights reserved.

关键词： Oxidation

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Prevention of external force damage to overhead power lines based on network and intelligent positioning

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第3期6卷 551-583页

作者： Xie, Bo-Lin Pan, Jeng-Shyang Lin, Yuh-Chung Xie, Yu-Hong Pan, Tien-Szu Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China School of Information Science and Technology Sanda University Shanghai201209 China Guangdong Power Grid Heyuan Power Supply Bureau Transmission Management Office China Southern Power Grid Heyuan517000 China Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung807 Taiwan

In the research on the prevention of external damage to power transmission lines, it is a difficult problem to prevent large machinery vehicles from damaging the overhead transmission lines. After the emergence of video surveillance technology, the accident rate has dropped significantly. But still unable to completely eliminate such accidents. The anti-external damage solution based on network and intelligent positioning is very effective for early warning of accidents. This gives us the opportunity to completely eliminate such accidents. In this paper we utilize the satellite positioning data of the power towers to establish a warning zone along the power line. When the system detects that some large machinery vehicles enter the warning zone, early warning messages will be sent to these vehicles. Afterward, the connection between vehicle drivers and management center can be established such that further instructions can be ordered to avoid accidents. According to our simulations, it presents our proposed mechanism can effectively prevent the damage occurrences which may be caused by large machinery vehicles. © 2021 Journal of Network Intelligence.

关键词： Accidents

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Personalizing search results using hierarchical RNN with query-aware attention

arXiv

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arXiv 2019年

作者： Ge, Songwei Dou, Zhicheng Jiang, Zhengbao Nie, Jian-Yun Wen, Ji-Rong School of Information Renmin University of China DIRO Université de Montréal Beijing Key Laboratory of Big Data Management and Analysis Methods National Engineering Laboratory of Big Data System Software Beijing Institute of Technology Key Laboratory of Data Engineering and Knowledge Engineering MOE

Search results personalization has become an effective way to improve the quality of search engines. Previous studies extracted information such as past clicks, user topical interests, query click entropy and so on to tailor the original ranking. However, few studies have taken into account the sequential information underlying previous queries and sessions. Intuitively, the order of issued queries is important in inferring the real user interests. And more recent sessions should provide more reliable personal signals than older sessions. In addition, the previous search history and user behaviors should influence the personalization of the current query depending on their relatedness. To implement these intuitions, in this paper we employ a hierarchical recurrent neural network to exploit such sequential information and automatically generate user profile from historical data. We propose a query-aware attention model to generate a dynamic user profile based on the input query. Significant improvement is observed in the experiment with data from a commercial search engine when compared with several traditional personalization models. Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training. Copyright © 2019, The Authors. All rights reserved.

关键词： Search engines

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