Relay Selection Strategy in the Secure Cooperative Communications System

With the low cost and low hardware complex considerations,cooperative systems are a tendency in the future communications.This work considers the secure cooperative communications systems.For a practical situation in the system,the scenario includes multiple source stations,multiple relay stations,multiple destination stations,and eavesdroppers.To analyze the optimal relay selection in the system,we begin with the performance analysis for a single source station and a single destination station.By applying two cooperative models,the amplify-andforward（AF） mode and decode-and-forward（DF）mode,the secrecy capacity is derived.Then,we apply the derived results to the considered environment to find the optimal relay assignment.By the way,the relay selection can be obtained by the exhaustive search algorithm.However,there are a lot of steps needed if the number of source stations is large.Hence,applying the characters of the cooperative modes in the relay selection,the pre-selection step is proposed with a mathematical derivation.It could be used for the practical situation without a long-time calculation.

Parallelizing Modified Cuckoo Search on MapReduce Architecture

Meta-heuristics typically takes long time to search optimality from huge amounts of data samples for applications like communication, medicine, and civil engineering. Therefore, parallelizing meta-heuristics to massively reduce runtime is one hot topic in related research. In this paper, we propose a MapReduce modified cuckoo search （MRMCS）, an efficient modified cuckoo search （MCS） implementation on a MapReduce architecture--Hadoop. MapReduce particle swarm optimization （MRPSO） from a previous work is also implemented for comparison. Four evaluation functions and two engineering design problems are used to conduct experiments. As a result, MRMCS shows better convergence in obtaining optimality than MRPSO with two to four times speed-up.

DynamicRetriever:A Pre-trained Model-based IR System Without an Explicit Index

Web search provides a promising way for people to obtain information and has been extensively studied.With the surge of deep learning and large-scale pre-training techniques,various neural information retrieval models are proposed,and they have demonstrated the power for improving search(especially,the ranking)quality.All these existing search methods follow a common paradigm,i.e.,index-retrieve-rerank,where they first build an index of all documents based on document terms(i.e.,sparse inverted index)or representation vectors(i.e.,dense vector index),then retrieve and rerank retrieved documents based on the similarity between the query and documents via ranking models.In this paper,we explore a new paradigm of information retrieval without an explicit index but only with a pre-trained model.Instead,all of the knowledge of the documents is encoded into model parameters,which can be regarded as a differentiable indexer and optimized in an end-to-end manner.Specifically,we propose a pre-trained model-based information retrieval(IR)system called DynamicRetriever,which directly returns document identifiers for a given query.Under such a framework,we implement two variants to explore how to train the model from scratch and how to combine the advantages of dense retrieval models.Compared with existing search methods,the model-based IR system parameterizes the traditional static index with a pre-training model,which converts the document semantic mapping into a dynamic and updatable process.Extensive experiments conducted on the public search benchmark Microsoft machine reading comprehension(MS MARCO)verify the effectiveness and potential of our proposed new paradigm for information retrieval.

Analyses of Political Crisis Impact on Tourism:A Panel Counterfactual Approach with Internet Search Index

Existing research has shown that political crisis events can directly impact the tourism industry.However,the current methods suffer from potential changes of unobserved variables,which poses challenges for a reliable evaluation of the political crisis impacts.This paper proposes a panel counterfactual approach with Internet search index,which can quantitatively capture the change of crisis impacts across time and disentangle the effect of the event of interest from the rest.It also provides a tool to examine potential channels through which the crisis may affect tourist outflows.This research empirically applies the framework to analyze the THAAD event on tourist flows from the Chinese Mainland to South Korea.Findings highlight the strong and negative short-term impact of the political crisis on the tourists' intentions to visit a place.This paper provides essential evidence to help decision-makers improve the management of the tourism crisis.

Early Warning and Monitoring of Coronavirus Disease 2019 Using Baidu Search Index and Baidu Information Index in Guangxi,China

Coronavirus disease 2019(COVID-19)is an emerging infectious disease,and it is important to detect early and monitor the disease trend for policymakers to make informed decisions.We explored the predictive utility of Baidu Search Index and Baidu Information Index for early warning of COVID-19 and identified search keywords for further monitoring of epidemic trends in Guangxi.A time-series analysis and Spearman correlation between the daily number of cases and both the Baidu Search Index and Baidu Information Index were performed for seven keywords related to COVID-19 from January 8 to March 9,2020.The time series showed that the temporal distributions of the search terms“coronavirus,”“pneumonia”and“mask”in the Baidu Search Index were consistent and had 2 to 3 days'lead time to the reported cases;the correlation coefficients were higher than 0.81.The Baidu Search Index volume in 14 prefectures of Guangxi was closely related with the number of reported cases;it was not associated with the local GDP.The Baidu Information Index search terms“coronavirus”and“pneumonia”were used as frequently as 192,405.0 and 110,488.6 per million population,respectively,and they were also significantly associated with the number of reported cases(rs>0.6),but they fluctuated more than for the Baidu Search Index and had 0 to 14 days'lag time to the reported cases.The Baidu Search Index with search terms“coronavirus,”“pneumonia”and“mask”can be used for early warning and monitoring of the epidemic trend of COVID-19 in Guangxi,with 2 to 3 days'lead time.

“黑天鹅”和“灰犀牛”事件对原油市场的冲击效应测算:GSI-BN研究框架

波谲云诡的国际形势及多变的全球市场环境,伴随着一系列的“黑天鹅”、“灰犀牛”突发事件.重大突发事件的冲击效应测算及价格拐点预测一直是学术界特别关心的热点和难点问题之一.本文提出了一个新的研究框架GSI-BN来分析重大突发事件对原油市场的冲击效应并预测不同事件发生时油价的走势.首先,基于谷歌搜索指数(Google Search Index,GSI)构建突发事件网络舆情关注度指标,确定不同种类的突发事件的时间窗.其次,引入贝叶斯网络(Bayesian Network,BN),将突发事件简化到拓扑网络图上,细分突发事件并挖掘事件及其背后的条件概率,分析突发事件影响机制并预测其发生概率;最后,基于情景预判分析预测不同情景下突发事件所导致的油价走势.实证结果表明:当供给和需求的月均增速都较高时,供需仍处于均衡状态,油价在低价格区间的概率最大;当供给冲击较大,需求处于正常水平增速时,油价处于中低价格区间的概率最大;需求侧方面,当金融危机发生时,原油消费量的次月增速绝对值在中速增长区间的概率最大;供给侧方面,两种或三种突发事件同时发生都是小概率事件.此外,随着OPEC致力于减产,全球石油需求走高.飓风对价格的影响相较于往年逐渐变小.金融危机对原油市场的影响是全面的,只有部分影响会通过需求冲击传导到价格.战争和OPEC会议都是短暂的供给冲击,更多的是反映了市场的预期,传导到价格时,不会产生较大的价差.本文为研究突发事件对原油市场的冲击效应及油价拐点提供了一个新的视角和方法.

The impact of fundamental factors and sentiments on the valuation of cryptocurrencies

The valuation of cryptocurrencies is important given the increasing significance of this potential asset class.However,most state-of-the-art cryptocurrency valuation methods only focus on one of the fundamental factors or sentiments and use out-of-date data sources.In this study,a robust cryptocurrency valuation method is developed using up-to-date datasets.Using various panel regression models and moving-window regression tests,the impacts of fundamental factors and sentiments in the valuation of cryptocurrencies are explored with data covering from January 1,2009 to April 30,2023.The research shows the importance of sentiments and suggests that the fear and greed index can indicate when to make a cryptocurrency investment,while Google search interest in cryptocurrency is crucial when choosing the appropriate type of cryptocurrency.Moreover,consensus mechanism and initial coin offering have significant effects on cryptocurrencies without stablecoins,while their impacts on cryptocurrencies with stablecoins are insignificant.Other fundamental factors,such as the type of supply and the presence of smart contracts,do not have a significant influence on cryptocurrency.Findings from this study can enhance cryptocurrency marketisation and provide insightful guidance for investors,portfolio managers,and policymakers in assessing the utility level of each cryptocurrency.