110023 research outputs found
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Monitoring Spatial Keyword Queries Based on Resident Domains of Mobile Objects in IoT Environments
No full text[[abstract]]In IoT environments, geo-tagged data have rapidly been emerging as smart things, e.g., mobile devices or connected cars, are generally equipped with the global positioning system (GPS) module. A large volume of geo-tagged data can be fundamental to providing applications of location-based services (LBSs). One of the important LBS applications is to provide continuous spatial keyword queries. A continuous spatial keyword query monitors a designated region with a set of keywords. In the designated region, if mobile objects contain all the keywords of the query, they are the answer set for the query. The query continuously monitors the spatial region and reports its up-to-date query result. This paper presents a resident-domain-based approach for continuously monitoring spatial keyword queries. The proposed approach shifts the monitoring of tasks of affected queries from the server to mobile objects which have computational and storage abilities. According to their computational ability, the proposed approach assigns as large as possible resident domains to mobile objects. Within the resident domain, the mobile object informs the server about its spatial information only when crossing the boundary of its monitored queries, thereby reducing the communication cost between it and the server. The experimental evaluation has verified that the proposed approach outperforms the existing approach
A Time-Driven Cloudlet Placement Strategy for Workflow Applications in Wireless Metropolitan Area Networks
No full text[[abstract]]With the rapid development of mobile technology, mobile applications have increasing requirements for computational resources, and mobile devices can no longer meet these requirements. Mobile edge computing (MEC) has emerged in this context and has brought innovation into the working mode of traditional cloud computing. By provisioning edge server placement, the computing power of the cloud center is distributed to the edge of the network. The abundant computational resources of edge servers compensate for the lack of mobile devices and shorten the communication delay between servers and users. Constituting a specific form of edge servers, cloudlets have been widely studied within academia and industry in recent years. However, existing studies have mainly focused on computation offloading for general computing tasks under fixed cloudlet placement positions. They ignored the impact on computation offloading results from cloudlet placement positions and data dependencies among mobile application components. In this paper, we study the cloudlet placement problem based on workflow applications (WAs) in wireless metropolitan area networks (WMANs). We devise a cloudlet placement strategy based on a particle swarm optimization algorithm using genetic algorithm operators with the encoding library updating mode (PGEL), which enables the cloudlet to be placed in appropriate positions. The simulation results show that the proposed strategy can obtain a near-optimal cloudlet placement scheme. Compared with other classic algorithms, this algorithm can reduce the execution time of WAs by 15.04-44.99%
Monthly Revenue Forecast of Exchange Listed Companies in Taiwan: Taking the Yuanta/P-shares Taiwan Top 50 ETF Components as Examples
No full text[[abstract]]股票上市公司的基本面是影響股票市場長期發展趨勢的主因，以長期投資股票市場的角度，上市公司的獲利與盈餘終將反應其市場價值，而營業收入（以下簡稱營收）正是公司獲利的主要來源。營收具有資訊內涵，因此，上市公司未來月營收的預測，能給予投資者提早取得有意義的資訊內涵。本研究為了進一步提升預測的準確性，對傳統ARIMA模型進行改良，並實施幾種不同演算法的預測效能比較，以平均絕對百分比誤差（MAPE）評估各方法之預測準確度。最後以最佳預測方法預測上市公司未來12個月之月營收，提供股票市場長期投資者事先了解該公司未來月營收可能的成長趨勢及其投資上的參考。[[abstract]]The fundamentals of listed stock companies are the main factors affecting the long-term development trend of the stock market. From the perspective of long-term investment in the stock market, the profits and earnings of listed companies will eventually reflect their market value. Operating revenue has information connotation and is the main source of company profits. Therefore, the forecast of future monthly revenue of listed companies can give investors early access to meaningful information. In order to further improve the accuracy of forecasting, this study improves the traditional ARIMA model and implements the comparison of the prediction performance of several different algorithms. The average absolute percentage error (MAPE) is used to evaluate the forecast accuracy of each algorithm. Finally, the best algorithms are used to forecast the monthly revenue of the listed company in Taiwan in the next 12 months. The results provide long-term investors in the stock market with a prior understanding of the company's future monthly revenue growth trend and investment reference
Novel Graph-Based Machine Learning Technique to Secure Smart Vehicles in Intelligent Transportation Systems
No full text[[abstract]]Intelligent Transport Systems (ITS) is a developing technology that will significantly alter the driving experience. In such systems, smart vehicles and Road-Side Units (RSUs) communicate through the VANET. Safety apps use these data to identify and prevent hazardous situations in real-time. Detection of malicious nodes and attack traffic in Intelligent Transportation Systems (ITS) is a current research subject. Recently, researchers are proposing graph-based machine learning techniques to identify malicious users in the ITS environment, through which it is easy to analyze the network traffic and detect the malicious devices. Therefore, graph-based machine learning techniques could be a technique that efficiently detect malicious nodes in the ITS environment. In this context, this article aims to provide a technique for resolving authentication and security issues in ITS using lightweight cryptography and graph-based machine learning. Our solution uses the concepts of identity based authentication technique and graph-based machine learning in order to provide authentication and security to the smart vehicle in ITS. By authenticating smart vehicles in ITS and identifying various cyber threats, our proposed method substantially contributes to the development of intelligent transportation communication environment
Personal Mobility in Metaverse With Autonomous Vehicles Using Q-Rung Orthopair Fuzzy Sets Based OPA-RAFSI Model
No full text[[abstract]]The term metaverse, which shows a 3D-designed virtual medium where people can connect through their avatars to spend time, telecommute, and socialize, has entered our lives fast. There are limitless implementations that can take place in the metaverse. Integration of another technological innovation, which is autonomous vehicles to the metaverse, is at hand. There are numerous alternative uses of autonomous vehicles in the metaverse. In this study, three alternative implementation options for autonomous vehicles in the metaverse are investigated. These alternatives are evaluated using the proposed multi-criteria decision-making (MCDM) method under twelve different criteria, which are grouped under four main aspects, namely technological, societal, legal and ethical, and transportation. A novel hybrid model based on q-rung orthopair fuzzy sets (q-ROFSs) which consists of three stages is presented to express the framework definition, calculate the weight coefficients of the criteria, and rank various alternatives. In the first stage, the structure of the problem is created. In the second stage, q-ROFSs based OPA algorithm is used to calculate the weights of the criteria. In the last stage, q-ROFSs based RAFSI (Ranking of Alternatives through Functional mapping of criterion sub-intervals into a Single Interval) is applied to choose the best alternative among the three alternatives. Finally, we present a case study to verify our proposed method. The results of this study have the potential to be used as a guide by decision-makers of the metaverse while integrating autonomous vehicles into the transportation system
Phishing Website Detection With Semantic Features Based on Machine Learning Classifiers: A Comparative Study
No full text[[abstract]]The phishing attack is one of the main cybersecurity threats in web phishing and spear phishing. Phishing websites continue to be a problem. One of the main contributions to our study was working and extracting the URL & Domain Identity feature, Abnormal Features, HTML and JavaScript Features, and Domain Features as semantic features to detect phishing websites, which makes the process of classification using those semantic features, more controllable and more effective. The current study used machine learning model algorithms to detect phishing websites, and comparisons were made. We have used 16 machine learning models adopted with 10 semantic features that represent the most effective features for the detection of phishing webpages extracted from two datasets. The GradientBoostingClassifier and RandomForestClassifier had the best accuracy based on the comparison results (i.e., about 97%). In contrast, GaussianNB and the stochastic gradient descent (SGD) classifier represent the lowest accuracy results; 84% and 81% respectively, in comparison with other classifiers
Smart defense against distributed Denial of service attack in IoT networks using supervised learning classifiers
No full text[[abstract]]From smart home to industrial automation to smart power grid, IoT- based solutions penetrate into every working field. These devices expand the attack surface and turned out to be an easy target for the attacker as resource constraint nature hinders the integration of heavy security solutions. Because IoT devices are less secured and operate mostly in unattended scenario, they perfectly justify the requirements of attacker to form botnet army to trigger Denial of Service attack on massive scale. Therefore, this paper presents a Machine Learning-based attack detection approach to identify the attack traffic in Consumer IoT (CIoT). This approach operates on local IoT network-specific attributes to empower low-cost machine learning classifiers to detect attack, at the local router. The experimental outcomes unveiled that the proposed approach achieved the highest accuracy of 0.99 which confirms that it is robust and reliable in IoT networks
A Study on the Advertising Effectiveness of Video Game Live –The case of Battle Royale Game
No full text[[abstract]]本研究希望以《Apex 英雄》的爆紅探討直播影響的顯著性和範例性，希望為台灣的線上遊戲和直播產業在日後宣傳與直播產業帶來提供實務界與學界研究上的參考。
以google的網路問卷針對遊戲《Apex 英雄》的玩家蒐集資料以進行回收調查。回收合計513份有回答問卷，過濾掉之中無效問卷13份，得到500份的有效問卷。
（一）本次調查發現玩家的以18至24歲的男性為多數；其中玩家多為無收入並且在線上遊戲中的每個月中開銷少於平均1000 元的大學或大專院校學歷的學生族群；當中多數人擁有三年以上遊戲資歷並且擁有每天以1 至3 小時的遊戲時間。
（二）在玩家的動機調查指出多數玩家遊玩遊戲的主要目的為獲得成就感、紓發壓力、放鬆身心及結交的遊戲同好，對消費成因較具顯著影響。
（三）在玩家的消費行為調查之中，收入與遊玩的遊戲量與購買遊戲中的遊戲商品和道具的意願也越高，對消費成因不具顯著影響。
（四）在遊戲直播因素的研究中，玩家多藉由直播了解新遊戲的風格同時會為了精進自己的遊戲技術去研究直播主們的遊戲技巧並會在過程中受直播影響進而嘗試去新遊戲，對消費成因具影響性。[[abstract]]This study hopes to use the popularity of "Apex Legends" to explore the significance and exemplary impact of live broadcasting, and hopes to provide practical and academic research references for Taiwan's online game and live broadcasting industries in the future promotion and live broadcasting industries. Use google's online questionnaire to collect data from players of the game "Apex Legends" for recycling surveys. A total of 513 questionnaires with answers were recovered, 13 invalid questionnaires were filtered out, and 500 valid questionnaires were obtained.
(1) This survey found that the majority of players are males aged 18 to 24; most of the players are students with university or college degrees who have no income and spend less than an average of 1,000 yuan per month in online games Ethnic groups; most of them have more than three years of gaming experience and have 1 to 3 hours of gaming time per day.
(2) According to the survey on players' motivation, the main purpose of most players playing games is to gain a sense of accomplishment, relieve stress, relax and make friends with gamers, which has a significant impact on the cause of consumption.
(3) In the investigation of the consumption behavior of players, the higher the income, the amount of games played and the willingness to purchase game products and props in the game, the higher the consumption causes.
(4) In the study of game live broadcast factors, players mostly learn about the style of new games through live broadcast. At the same time, they will study the game skills of live broadcasters in order to improve their game technology, and will be affected by the live broadcast in the process and try to play new games. , has an influence on the causes of consumption
Explore the Electronic Toll Collection Service Quality by Kano Model - A Case Study of Far Eastern Electronic Toll Collection Company
No full text[[abstract]]台灣十大建設之一「中山高速公路」，早期主要的收費方式為傳統人工收費，但屢有民意反映，居住所在地或使用不同造成高速公路上/下交流道有繳費不公平的情形，故反映未來實施計程收費可落實公平付費。而國道全線於民國102年12月30日正式轉換實施計程電子收費制度，主張走多少、付多少的概念，其目的可實現用路人實現公平使用者付費原則。故透過檢視國道電子收費所提供之ETC服務項目，以瞭解是否真正符合用路人需求，此乃本文主要研究目的。本文以「遠通電收公司」為研究個案，輔以PZB「服務品質量表」（SERVQUAL）編製遠通電收公司之ETC服務項目，以分析遠通電收公司服務措施元素，並以Kano模式歸納二維品質特性，據以調整ETC服務措施策略，期能精準提升遠通電收公司之ETC服務品質，達到有效服務之效果。經研究結果顯示，遠通電收公司「魅力品質」之服務項目計有 6 項，具備「一元化品質」要素有 5 項， 「當然品質」有 9 項，「無差異品質」要素有 2 項，無「反向品質」要素服務項目。故建議遠通電收公司應增加屬「魅力品質要素」項目，改善及加強屬「一元品質要素」項目，維持屬「當然品質要素」項目，適度降低「無差異品質要素」項目，作為遠通電收公司經營資源重新配置之參據。[[abstract]]Sun Yat-sen Freeway (National Freeway No.1) is one of Ten Major Infrastructure Projects in Taiwan. We took manual toll collection inaner at the beginning, but somehow there’s sometime had feedback which the system isn’t fair enough for road users because the living or driving distances aren’t always the same but eventually have to be charged the same. Therefore when The National Freeway Bureau launches its new "Pay as You Go" toll system, it can perfectly avoid the problem it used to cause and would become fair enough for every citizen. From 30th Dec, 2013 the Electronic Toll Collection has officially launched, and main concept of the system is "Pay as You Go", the purpose is to achieve the most economically efficient and to be a fair system for road users. Therefore, by case studying the Electronic Toll Collection of freeway in Taiwan and find out if it’s truly meet road users’ needs, which are the most important research purpose. The subject of this case study is Far Eastern Electronic Toll Collection Company and we used Service Quality model (SERVQUAL) to analysis service items providing by this company and also summarizing through the Perspectives of Kano Two-Dimensional Quality Model, to adjust the strategy of service measures of Electronic Toll Collection, and to precisely improve the service quality of Far Eastern Electronic Toll Collection Company, make it more efficiently. From the research we could find the Far Eastern Electronic Toll Collection company has 6 attractive quality elements, 5 one-dimensional quality elements, 9 must-be quality elements and 2 at indifferent quality elements, and they don’t have any service items achieve reversal quality. Base on the result, we advise Far Eastern Electronic Toll Collection company should increase attractive quality, improve and strengthen the service items for one-dimensional quality, remain the must-be quality and reduce the indifferent quality in moderation; this could be the reference to reconfigure management resources
The Effects of Consumer Cognition on Purchase Intentions for Products of Social Enterprise – Moderating Effect of Empathy
No full text[[abstract]]近十幾年來，由於經濟不景氣，台灣非營利組織在政府補助與外界資金挹注不足的情況下，迫使許多非營利組織另尋出路，以企業經營的方式注入資金，讓組織得以永續經營，於是結合社會使命與追求利潤的「社會企業」便因應而生。本研究欲探討消費者在面對社會企業產品時，消費者社會企業認知對社會企業產品購買意願的影響，並探討同理心對兩者之間是否具有調節效果。本研究以購買庇護工場產品之消費者為主要研究對象，採用問卷調查法蒐集資料，透過階層迴歸分析驗證研究假說。研究結果顯示，35歲以下女性、曾參與義工經驗之消費者，具有較高的同理心；有參與義工經驗的女性消費者，購買社會企業產品意願較高；消費者社會企業認知可促進消費者對社會企業產品之購買意願；消費者同理心對社會企業認知與社會企業產品購買意願之間具有調節效果，其中高同理心消費者較低同理心消費者，具有較高購買社會企業產品意願。故社會企業經營應推廣義工活動，提升消費者同理心與社會企業產品購買意願；同時應強化消費者同理心和社會企業認知，有助於提升社會企業產品購買意願。[[abstract]]In recent years, due to the economic depression and under the circumstance of insufficient government subsidy and external funding, nonprofit organizations in Taiwan were forced to find other ways of getting more funding for the purpose of the sustainable development of organization. Therefore, social enterprises generated, which combine social mission with the pursuit of profit. The study will explore that if consumers’ cognitions of social enterprises affect purchase intention when consumers see products of social enterprises, and if the empathy modulates between consumers’ cognitions and purchase intention.Consumers who purchase products of sheltered workshops were the main target. The questionnaire was used to collect data, and the revalidation hypothesis was analyzed by the hierarchical regression analysis. The result shows that the female consumers under the age of 35 who have participated in volunteer service express more empathy; besides, the participating in volunteer service female consumers have more willingness to buy the products of social enterprises.Consumer cognition of social enterprise can promote the consumer purchase intention to buy the products of social enterprises. Moreover, consumer empathy has a moderating effect between the social enterprise cognition and purchase intentions for products of social enterprises. Of which the advanced empathy consumers express higher intention to purchase the products of social enterprises than the primary empathy consumers. Consequently, social enterprises should promote volunteers’ activities in order to reinforce consumers’ empathy and increase the purchase intention of social enterprises’ product; at the same time, to intensify consumers’ empathy and cognition of social enterprises helps the purchase intention of social enterprises’ products