RL and AHP-Based Multi-Timescale Multi-Clock Source Time Synchronization for Distribution Power Internet of Things

Time synchronization(TS)is crucial for ensuring the secure and reliable functioning of the distribution power Internet of Things(IoT).Multi-clock source time synchronization(MTS)has significant advantages of high reliability and accuracy but still faces challenges such as optimization of the multi-clock source selection and the clock source weight calculation at different timescales,and the coupling of synchronization latency jitter and pulse phase difference.In this paper,the multi-timescale MTS model is conducted,and the reinforcement learning(RL)and analytic hierarchy process(AHP)-based multi-timescale MTS algorithm is designed to improve the weighted summation of synchronization latency jitter standard deviation and average pulse phase difference.Specifically,the multi-clock source selection is optimized based on Softmax in the large timescale,and the clock source weight calculation is optimized based on lower confidence bound-assisted AHP in the small timescale.Simulation shows that the proposed algorithm can effectively reduce time synchronization delay standard deviation and average pulse phase difference.

EvolveKG:a general framework to learn evolving knowledge graphs

A great many practical applications have observed knowledge evolution,i.e.,continuous born of new knowledge,with its formation influenced by the structure of historical knowledge.This observation gives rise to evolving knowledge graphs whose structure temporally grows over time.However,both the modal characterization and the algorithmic implementation of evolving knowledge graphs remain unexplored.To this end,we propose EvolveKG–a general framework that enables algorithms in the static knowledge graphs to learn the evolving ones.EvolveKG quantifies the influence of a historical fact on a current one,called the effectiveness of the fact,and makes knowledge prediction by leveraging all the cross-time knowledge interaction.The novelty of EvolveKG lies in Derivative Graph–a weighted snapshot of evolution at a certain time.Particularly,each weight quantifies knowledge effectiveness through a temporarily decaying function of consistency and attenuation,two proposed factors depicting whether or not the effectiveness of a fact fades away with time.Besides,considering both knowledge creation and loss,we obtain higher prediction accuracy when the effectiveness of all the facts increases with time or remains unchanged.Under four real datasets,the superiority of EvolveKG is confirmed in prediction accuracy.

Human-machine interactive streaming anomaly detection by online self-adaptive forest

At nomaly detectors are used to distinguish differences between normal and abnormal data,which are usually implemented by evaluating and ranking the anomaly scores of each instance.A static unsupervised streaming anomaly detector is difficult to dynamically adjust anomaly score calculation.In real scenarios,anomaly detection often needs to be regulated by human feedback,which benefits adjusting anomaly detectors.In this paper,we propose a human-machine interactive streaming anomaly detection method,named ISPForest,which can be adaptively updated online under the guidance of human feedback.In particular,the feedback will be used to adjust the anomaly score calculation and structure of the detector,ideally attaining more accurate anomaly scores in the future.Our main contribution is to improve the tree-based streaming anomaly detection model that can be updated online from perspectives of anomaly score calculation and model structure.Our approach is instantiated for the powerful class of tree-based streaming anomaly detectors,and we conduct experiments on a range of benchmark datasets.The results demonstrate that the utility of incorporating feedback can improve the performance of anomaly detectors with a few human efforts.

Quantifying predictability of sequential recommendation via logical constraints

The sequential recommendation is a compelling technology for predicting users’next interaction via their historical behaviors.Prior studies have proposed various methods to optimize the recommendation accuracy on different datasets but have not yet explored the intrinsic predictability of sequential recommendation.To this end,we consider applying the popular predictability theory of human movement behavior to this recommendation context.Still,it would incur serious bias in the next moment measurement of the candidate set size,resulting in inaccurate predictability.Therefore,determining the size of the candidate set is the key to quantifying the predictability of sequential recommendations.Here,different from the traditional approach that utilizes topological constraints,we first propose a method to learn inter-item associations from historical behaviors to restrict the size via logical constraints.Then,we extend it by 10 excellent recommendation algorithms to learn deeper associations between user behavior.Our two methods show significant improvement over existing methods in scenarios that deal with few repeated behaviors and large sets of behaviors.Finally,a prediction rate between 64%and 80%has been obtained by testing on five classical datasets in three domains of the recommender system.This provides a guideline to optimize the recommendation algorithm for a given dataset.

Preface

As the chairs of the 8th International Conference of Pioneering Computer Scientists,Engineers and Educators 2023(ICPCSEE 2023,originally ICYCSEE),it is our great pleasure to welcome you to the conference proceedings.ICPCSEE 2023 was held in Harbin,China,during September 22–24,2023,and hosted by the Harbin Engineering University,the Harbin Institute of Technology,the Northeast Forestry University,the Harbin University of Science and Technology,the Heilongjiang Computer Federation,and the National Academy of Guo Ding Institute of Data Sciences.The goal of this conference series is to provide a forum for computer scientists,engineers,and educators.

Preface

As the chairs of the 8th International Conference of Pioneering Computer Scientists,Engineers and Educators 2023(ICPCSEE 2023,originally ICYCSEE),it is our great pleasure to welcome you to the conference proceedings.ICPCSEE 2023 was held in Harbin,China,during September 22–24,2023,and hosted by the Harbin Engineering University,the Harbin Institute of Technology,the Northeast Forestry University,the Harbin University of Science and Technology,the Heilongjiang Computer Federation,and the National Academy of Guo Ding Institute of Data Sciences.The goal of this conference series is to provide a forum for computer scientists,engineers,and educators.

深度学习模型终端环境自适应方法研究

随着人工智能和物联网的快速发展与融合,智能物联网AIoT正成长为一个极具前景的新兴前沿领域,其中深度学习模型的终端运行是其主要特征之一.针对智能物联网应用场景动态多样,以及物联网终端(智能手机、可穿戴及其他嵌入式设备等)计算和存储资源受限等问题,深度学习模型环境自适应正成为一种新的模型演化方式.其旨在确保适当性能的条件下,能自适应地根据环境变化动态调整模型,从而降低资源消耗、提高运算效率.具体来说,它需要主动感知环境、任务性能需求和平台资源约束等动态需求,进而通过终端模型的自适应压缩、云边端模型分割、领域自适应等方法,实现深度学习模型对终端环境的动态自适应和持续演化.本文围绕深度学习模型自适应问题,从其概念、系统架构、研究挑战与关键技术等不同方面进行阐述和讨论,并介绍我们在这方面的研究实践.

电力系统安全仿真技术:工程安全、网络安全与信息物理综合安全

电力系统安全仿真是面向电力系统自身故障或外部攻击等安全威胁,通过仿真实验或借助数值计算研究系统行为的技术.随着自动控制、网络通信、人工智能等信息技术的广泛应用,电力系统已发展成为物理系统与信息系统深度耦合的信息物理融合系统.物理破坏或网络攻击产生的故障在电力系统中相互关联,可跨域传播,产生新的安全威胁形态.电力系统安全仿真正在面临新的挑战.本文回顾了历史上影响广泛的电力系统安全事件;从工程安全、网络安全、信息物理综合安全3个维度,分析电力系统安全仿真的需求和技术发展,对代表性的安全仿真平台进行分类和总结;探讨电力系统安全仿真技术面临的挑战和发展趋势.

A survey on ensemble learning

Despite significant successes achieved in knowledge discovery,traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data,such as imbalanced,high-dimensional,noisy data,etc.The reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure of data.In this context,it becomes an important topic in the data mining field that how to effectively construct an efficient knowledge discovery and mining model.Ensemble learning,as one research hot spot,aims to integrate data fusion,data modeling,and data mining into a unified framework.Specifically,ensemble learning firstly extracts a set of features with a variety of transformations.Based on these learned features,multiple learning algorithms are utilized to produce weak predictive results.Finally,ensemble learning fuses the informative knowledge from the above results obtained to achieve knowledge discovery and better predictive performance via voting schemes in an adaptive way.In this paper,we review the research progress of the mainstream approaches of ensemble learning and classify them based on different characteristics.In addition,we present challenges and possible research directions for each mainstream approach of ensemble learning,and we also give an extra introduction for the combination of ensemble learning with other machine learning hot spots such as deep learning,reinforcement learning,etc.

Mobile crowd sensing task optimal allocation： a mobility pattern matching perspective

With the proliferation of sensor-equipped portable mobile devices, Mobile CrowdSensing （MCS） using smart devices provides unprecedented opportunities for collecting enormous surrounding data. In MCS applications, a crucial issue is how to recruit appropriate participants from a pool of available users to accomplish released tasks, satisfying both resource efficiency and sensing quality. In order to meet these two optimization goals simultaneously, in this paper, we present a novel MCS task allocation framework by aligning existing task sequence with users＇ moving regularity as much as possible. Based on the process of mobility repetitive pattern discovery, the original task allocation problem is converted into a pattern matching issue, and the involved optimization goals are transformed into pattern matching length and support degree indicators. To determine a trade-off between these two competitive metrics, we propose greedy- based optimal assignment scheme search approaches, namely MLP, MDP, IU1 and IU2 algorithm, with respect to matching length-preferred, support degree-preferred and integrated utility, respectively. Comprehensive experiments on real- world open data set and synthetic data set clearly validate the effectiveness of our proposed framework on MCS task optimal allocation.

The mass,fake news,and cognition security

The widespread fake news in social networks is posing threats to social stability,economic development,and political democracy,etc.Numerous studies have explored the effective detection approaches of online fake news,while few works study the intrinsic propagation and cognition mechanisms of fake news.Since the development of cognitive science paves a promising way for the prevention of fake news,we present a new research area called Cognition Security(CogSec),which studies the potential impacts of fake news on human cognition,ranging from misperception,untrusted knowledge acquisition,targeted opinion/attitude formation,to biased decision making,and investigates the effective ways for fake news debunking.CogSec is a multidisciplinary research field that leverages the knowledge from social science,psychology,cognition science,neuroscience,AI and computer science.We first propose related definitions to characterize CogSec and review the literature history.We further investigate the key research challenges and techniques of CogSec,including human-content cognition mechanism,social influence and opinion dif-fusion,fake news detection,and malicious bot detection.Finally,we summarize the open issues and future research directions,such as the cognition mechanism of fake news,influence maximization of fact-checking information,early detection of fake news,fast refutation of fake news,and so on.

Cyber-physical-social collaborative sensing： from single space to cross-space

The development of wireless sensor network ing, social networking, and wearable sensing techniques has advanced the boundaries of research on understanding so cial dynamics. Collaborative sensing, which utilizes diver sity sensing and computing abilities across different entities, has become a popular sensing and computing paradigm. In this paper, we first review the history of research in col laborative sensing, which mainly refers to single space col laborative sensing that consists of physical, cyber, and so cial collaborative sensing. Afterward, we extend this concept into crossspace collaborative sensing and propose a general reference framework to demonstrate the distinct mechanism of crossspace collaborative sensing. We also review early works in crossspace collaborative sensing, and study the de tail mechanism based on one typical research work. Finally, although crossspace collaborative sensing is a promising re search area, it ＂is still in its infancy. Thus, we identify some key research challenges with potential technical details at the end of this paper.

Relative manifold based semi-supervised dimensionality reduction

A well-designed graph plays a fundamental role in graph-based semi-supervised learning; however, the topological structure of a constructed neighborhood is unstable in most current approaches, since they are very sensitive to the high dimensional, sparse and noisy data. This generally leads to dramatic performance degradation. To deal with this issue, we developed a relative manifold based semisupervised dimensionality reduction （RMSSDR） approach by utilizing the relative manifold to construct a better neighborhood graph with fewer short-circuit edges. Based on the relative cognitive law and manifold distance, a relative transformation is used to construct the relative space and the relative manifold. A relative transformation can improve the ability to distinguish between data points and reduce the impact of noise such that it may be more intuitive, and the relative manifold can more truly reflect the manifold structure since data sets commonly exist in a nonlinear structure. Specifically, RMSSDR makes full use of pairwise constraints that can define the edge weights of the neighborhood graph by minimizing the local reconstruction error and can preserve the global and local geometric structures of the data set. The experimental results on face data sets demonstrate that RMSSDR is better than the current state of the art comparing methods in both performance of classification and robustness.

Estimating posterior inference quality of the relational infinite latent feature model for overlapping community detection

Overlapping community detection has become a very hot research topic in recent decades,and a plethora of methods have been proposed.But,a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefined manually.We propose a flexible nonparametric Bayesian generative model for count-value networks,which can allow K to increase as more and more data are encountered instead of to be fixed in advance.The Indian buffet process was used to model the community assignment matrix Z,and an uncol-lapsed Gibbs sampler has been derived.However,as the community assignment matrix Zis a structured multi-variable parameter,how to summarize the posterior inference results andestimate the inference quality about Z,is still a considerable challenge in the literature.In this paper,a graph convolutional neural network based graph classifier was utilized to help tosummarize the results and to estimate the inference qualityabout Z.We conduct extensive experiments on synthetic data and real data,and find that empirically,the traditional posterior summarization strategy is reliable.

Spotlight: Hot Target Discovery and Localization with Crowdsourced Photos

Camera-equipped mobile devices are encouraging people to take more photos and the development and growth of social networks is making it increasingly popular to share photos online. When objects appear in overlapping Fields Of View(FOV), this means that they are drawing much attention and thus indicates their popularity. Successfully discovering and locating these objects can be very useful for many applications, such as criminal investigations, event summaries, and crowdsourcing-based Geographical Information Systems(GIS).Existing methods require either prior knowledge of the environment or intentional photographing. In this paper, we propose a seamless approach called 'Spotlight', which performs passive localization using crowdsourced photos.Using a graph-based model, we combine object images across multiple camera views. Within each set of combined object images, a photographing map is built on which object localization is performed using plane geometry. We evaluate the system’s localization accuracy using photos taken in various scenarios, with the results showing our approach to be effective for passive object localization and to achieve a high level of accuracy.

A framework based on sparse representation model for time series prediction in smart city

Smart city driven by Big Data and Internet of Things(loT)has become a most promising trend of the future.As one important function of smart city,event alert based on time series prediction is faced with the challenge of how to extract and represent discriminative features of sensing knowledge from the massive sequential data generated by IoT devices.In this paper,a framework based on sparse representa-tion model(SRM)for time series prediction is proposed as an efficient approach to tackle this challenge.After dividing the over-complete dictionary into upper and lower parts,the main idea of SRM is to obtain the sparse representation of time series based on the upper part firstly,and then realize the prediction of future values based on the lower part.The choice of different dictionaries has a significant impact on the performance of SRM.This paper focuses on the study of dictionary construction strategy and summarizes eight variants of SRM.Experimental results demonstrate that SRM can deal with different types of time series prediction flexibly and effectively.

CrowdDesigner:information-rich and personalized product description generation

1 Introduction Withthe rapid development of the E-commerce,more consumers turn to shop online.When online shoppers search for products using keywords,the related products appear with their brief descriptions.We conduct a real-world survey and find that most shoppers are dissatisfied with the existing"onefit-all product descriptions"and they have to spend more time to scan detail pages.However,handcrafting the attractive product descriptions is always costly.

Relative manifold based semi-supervised dimensionality reduction

Figure 8 of this article shows YaleB and CMU PIE with incorrect legend titles:YaleB(Tr=1900,Te=514,NOC=100)should be YaleB(Tr=1900,Te=514,d=100)(Fig.8(a));TIE(Tr=1200,Te=2880,d=100)should be PIE(Tr=1200,Te=2880,d=100)(Fig.8(b)).In Fig.9,the legend keys and the legend texts are mismatched.The correct figure is ilustrated as follows.