Analysis and assessment of a knowledge based smart city architecture providing service APIs

The main technical issues regarding smart city solutions are related to data gathering, aggregation, reasoning, data analytics, access, and service delivering via Smart City APIs (Application Program Interfaces). Different kinds of Smart City APIs enable smart city services and applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators, exploiting data analytics, and presenting services via APIs.Therefore, there is a strong activity on defining smart city architectures to cope with this complexity, putting in place a significant range of different kinds of services and processes. In this paper, the work performed in the context of Sii-Mobility smart city project on defining a smart city architecture addressing a wide range of processes and data is presented. To this end, comparisons of the state of the art solutions of smart city architectures for data aggregation and for Smart City API are presented by putting in evidence the usage semantic ontologies and knowledge base in the data aggregation in the production of smart services. The solution proposed aggregate and re-conciliate data (open and private, static and real time) by using reasoning/smart algorithms for enabling sophisticated service delivering via Smart City API. The work presented has been developed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with smart city services with the aim of reaching a more sustainable mobility and transport systems. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation, analytics support and service production exploiting smart city API. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Moreover, the proposed architecture has been assessed in terms of performance, computational and network costs in terms of measures that can be easily performed on private cloud on premise. The computational costs and workloads of the data ingestion and data analytics processes have been assessed to identify suitable measures to estimate needed resources. Finally, the API consumption related data in the recent period are presented.     

Emerging ICT concepts for smart,safe and sustainable industrial systems

This editorial introduces the special issue on Emerging Information and Communication Technology (ICT) concepts for smart, safe and sustainable industrial systems in the Elsevier journal Computers in Industry. The 13 papers in this special issue were selected because of their high quality and also because they propose emerging ICT solutions that address at least one of the three dimensions we suggest as basic requirements to design usable future Industrial Systems that must be safe, smart and sustainable. Previous global discussions about the state of the art with regard to the topic of this special issue are provided, as well as exploratory guidelines for future research in this area.     

Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.     

Preventing Recommendation Attack in Trust-Based Recommender Systems

Despite its success, similarity-based collaborative filtering suffers from some limitations, such as scalability, sparsity and recommendation attack. Prior work has shown incorporating trust mechanism into traditional collaborative filtering recommender systems can improve these limitations. We argue that trust-based recommender systems are facing novel recommendation attack which is different from the profile injection attacks in traditional recommender system. To the best of our knowledge, there has not any prior study on recommendation attack in a trust-based recommender system. We analyze the attack problem, and find that “victim” nodes play a significant role in the attack. Furthermore, we propose a data provenance method to trace malicious users and identify the “victim” nodes as distrust users of recommender system. Feasibility study of the defend method is done with the dataset crawled from Epinions website.     

Control for smart systems: Challenges and trends in smart cities

There have been tremendous developments in theories and technologies in control for smart systems. In this paper we review applications to various systems that are crucial for the future of smart cities, for example enterprise and manufacturing systems, transportation systems, energy systems, and data centres. Beyond discussing the existing technological trends and the methodological approaches developed so far for managing and controlling such systems, we also provide visions on the future challenges for these systems in these various aspects.     

Encouraging user participation in a course recommender system: An impact on user behavior

User participation emerged as a critical issue for collaborative and social recommender systems as well as for a range of other systems based on the power of user community. A range of mechanisms to encourage user participation in social systems has been proposed over the last few years; however, the impact of these mechanisms on users behavior in recommender systems has not been studied sufficiently. This paper investigates the impact of encouraging user participation in the context of CourseAgent, a community-based course recommender system. The recommendation power of CourseAgent is based on course ratings provided by a community of students. To increase the number of course ratings, CourseAgent applies an incentive mechanism which turns user feedback into a self-beneficial activity. In this paper, we describe the design and implementation of our course recommendation system and its incentive mechanism. We also report a dual impact of this mechanism on user behavior discovered in two user studies.     

突破智慧教育:基于图学习的课程推荐系统

近年来,随着互联网技术的迅猛发展,以慕课(MOOC)为代表的在线教育平台得到广泛普及.为助力“因材施教”的个性化智慧教育,以推荐算法为代表的人工智能技术受到了学术界与工业界的普遍关注.虽然在电子商务等领域获得了成功应用,但推荐算法与在线教育融合时仍面临严峻挑战:现有算法对隐式交互数据的挖掘不充足,推荐背后的知识指导作用不明显,面向实践的推荐系统软件有缺失.对此,设计了一套面向工业化场景的智慧课程推荐系统:(1)提出基于图卷积神经网络的推荐引擎,将“用户-课程”隐式交互数据建模为异构图;(2)将课程知识信息融入“用户-课程”异构图,深入挖掘了“用户-课程-知识”关联关系;(3)设计了高效的在线推荐系统,实现了“预处理-召回-离线排序-在线推荐-结果融合”的多段流水线原型,不仅能够快速响应课程推荐请求,更能有效缓解推荐算法落地的最大障碍——冷启动问题.最后,基于真实课程学习平台数据集,以对比实验表明了离线推荐引擎相比其他主流推荐算法的先进性,并基于两个典型用例分析验证了在线推荐系统面临工业场景需求的可用性.     

Smart sustainable cities – Exploring ICT solutions for reduced energy use in cities

This article explores the opportunities of using ICT as an enabling technology to reduce energy use in cities. An analytical framework is developed in which a typology of ICT opportunities is combined with a typology of household functions, i.e. all the activities that require energy. The energy used for household functions is calculated using a consumption-based lifecycle perspective. The analytical framework is intended to be of use to researchers, city and regional authorities and ICT companies interested in acquiring a better understanding of how ICT investments could contribute to reduce energy use in cities.     

知识驱动的推荐系统：现状与展望

个性化推荐系统能够根据用户的个性化偏好和需要,自动、快速、精准地为用户提供其所需的互联网资源,已成为当今大数据时代应用最广泛的信息检索系统,具有巨大的商业应用价值。近年来,随着互联网海量数据的激增,人工智能技术的快速发展与普及,以知识图谱为代表的大数据知识工程日益受到学界和业界的高度关注,也有力地推动推荐系统和个性化推荐技术也迈入到知识驱动与赋能的发展阶段。将知识图谱中蕴含的丰富知识作为有用的辅助信息引入推荐系统,不仅能够有效应对数据稀疏、语义失配等传统推荐系统难以避免的问题,还能帮助推荐系统产生多样化、可解释的推荐结果,并更好地完成跨领域推荐、序列化推荐等具有挑战性的推荐任务,从而提升各类实际推荐场景中的用户满意度。本文将现有融入知识图谱的各种推荐模型按其采用的推荐算法与面向的推荐场景不同进行分类,构建科学、合理的分类体系。其中,按照推荐方法的不同,划分出基于特征表示的和基于图结构的两大类推荐模型;按推荐场景划分,特别关注多样化推荐、可解释推荐、序列化推荐与跨领域推荐。然后,我们在各类推荐模型中分别选取代表性的研究工作进行介绍,还简要对比了各个模型的特点与优劣。此外,本文还结合当下人工智能技术和应用的发展趋势,展望了认知智能推荐系统的发展前景,具体包括融合多模态知识的推荐系统,具有常识理解能力的推荐系统,以及解说式、劝说式、抗辩式推荐系统。本文的综述内容和展望可作为推荐系统未来研究方向的有益参考。     

A context-aware recommender method based on text and opinion mining

A recommender system is an information filtering technology that can be used to recommend items that may be of interest to users. Additionally, there are the context-aware recommender systems that consider contextual information to generate the recommendations. Reviews can provide relevant information that can be used by recommender systems, including contextual and opinion information. In a previous work, we proposed a context-aware recommendation method based on text mining (CARM-TM). The method includes two techniques to extract context from reviews: CIET.5_embed, a technique based on word embeddings; and RulesContext, a technique based on association rules. In this work, we have extended our previous method by including CEOM, a new technique which extracts context by using aspect-based opinions. We call our extension of CARM-TOM (context-aware recommendation method based on text and opinion mining). To generate recommendations, our method makes use of the CAMF algorithm, a context-aware recommender based on matrix factorization. To evaluate CARM-TOM, we ran an extensive set of experiments in a dataset about restaurants, comparing CARM-TOM against the MF algorithm, an uncontextual recommender system based on matrix factorization; and against a context extraction method proposed in literature. The empirical results strongly indicate that our method is able to improve a context-aware recommender system.     

Modeling energy consumption in automated vacuum waste collection systems

In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. A smart city is a city in which information and communications technology are merged with traditional infrastructures, coordinated and integrated using new digital technologies. The increasing amount of waste generated, and the collection and treatment of waste poses a major challenge to modern urban planning in general, and to smart cities in particular. To cope with this problem, automated vacuum waste collection (AVWC) uses air suction on a closed network of underground pipes to transport waste from the drop off points scattered throughout the city to a central collection point, reducing greenhouse gas emissions and the inconveniences of conventional methods (odours, noise, etc.). Since a significant part of the cost of operating AVWC systems is energy consumption, we have developed a model with the aim of applying constraint programming technology to schedule the daily emptying sequences of the drop off points in such a way that energy consumption is minimized. In this paper we describe how the problem of deciding the drop off points that should be emptied at a given time can be modeled as a constraint integer programming (CIP) problem. Moreover, we report on experiments using real data from AVWC systems installed in different cities that provide empirical evidence that CIP offers a suitable technology for reducing energy consumption in AVWC.     

Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities

Recommender systems have been researched extensively over the past decades. Whereas several algorithms have been developed and deployed in various application domains, recent research efforts are increasingly oriented towards the user experience of recommender systems. This research goes beyond accuracy of recommendation algorithms and focuses on various human factors that affect acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. In this paper, we present an interactive visualization framework that combines recommendation with visualization techniques to support human-recommender interaction. Then, we analyze existing interactive recommender systems along the dimensions of our framework, including our work. Based on our survey results, we present future research challenges and opportunities.     

A hybrid recommendation approach for a tourism system

Many current e-commerce systems provide personalization when their content is shown to users. In this sense, recommender systems make personalized suggestions and provide information of items available in the system. Nowadays, there is a vast amount of methods, including data mining techniques that can be employed for personalization in recommender systems. However, these methods are still quite vulnerable to some limitations and shortcomings related to recommender environment. In order to deal with some of them, in this work we implement a recommendation methodology in a recommender system for tourism, where classification based on association is applied. Classification based on association methods, also named associative classification methods, consist of an alternative data mining technique, which combines concepts from classification and association in order to allow association rules to be employed in a prediction context. The proposed methodology was evaluated in some case studies, where we could verify that it is able to shorten limitations presented in recommender systems and to enhance recommendation quality.     

智慧城市开创IT产业发展的新局面

随着我国各地的智慧城市建设已正式迈入“实践探索”阶段,智慧城市作为一种区域经济发展新模式催生出新兴的产业概念--智慧城市IT产业。本文精选7个样本城市,分析智慧城市建设对IT产业发展的作用,具有一定代表性和示范作用,可供国内其他城市在开展智慧城市建设的过程中把握发展城市IT产业的机遇参考借鉴。     

Incremental critiquing

Conversational recommender systems guide users through a product space, alternatively making concrete product suggestions and eliciting the user's feedback. Critiquing is a common form of user feedback, where users provide limited feedback at the feature-level by constraining a feature's value-space. For example, a user may request a cheaper product, thus critiquing the price feature. Usually, when critiquing is used in conversational recommender systems, there is little or no attempt to monitor successive critiques within a given recommendation session. In our experience this can lead to inefficiencies on the part of the recommender system, and confusion on the part of the user. In this paper we describe an approach to critiquing that attempts to consider a user's critiquing history, as well as their current critique, when making new recommendations. We provide experimental evidence to show that this has the potential to significantly improve recommendation efficiency.     

The impact of consumer preferences on the accuracy of collaborative filtering recommender systems

Despite the omnipresent use of recommender systems in electronic markets, previous research has not analyzed how consumer preferences affect the accuracy of recommender systems. Markets, however, are characterized by a certain structure of consumers’ preferences. Consequently, it is not known in which markets recommender systems perform well. In this paper, we introduce a microeconomic model that allows a systematical analysis of different structures of consumers’ preferences. We develop a model-specific metric to measure the recommendation accuracy. We employ our model in a simulation to evaluate the impact of the structure of the consumers’ preferences on the accuracy of a popular collaborative filtering algorithm. Our study shows that recommendation accuracy is significantly affected by the similarity and number of consumer types and the distribution of consumers. The investigation reveals that in certain markets even random product recommendations outperform the collaborative filtering algorithm.     

CitySpec with shield: A secure intelligent assistant for requirement formalization

An increasing number of monitoring systems have been developed in smart cities to ensure that a city’s real-time operations satisfy safety and performance requirements. However, many existing city requirements are written in English with missing, inaccurate, or ambiguous information. There is a high demand for assisting city policymakers in converting human-specified requirements to machine-understandable formal specifications for monitoring systems. To tackle this limitation, we build CitySpec (Chen et al., 2022), the first intelligent assistant system for requirement specification in smart cities. To create CitySpec, we first collect over 1,500 real-world city requirements across different domains (e.g., transportation and energy) from over 100 cities and extract city-specific knowledge to generate a dataset of city vocabulary with 3,061 words. We also build a translation model and enhance it through requirement synthesis and develop a novel online learning framework with shielded validation. The evaluation results on real-world city requirements show that CitySpec increases the sentence-level accuracy of requirement specification from 59.02% to 86.64%, and has strong adaptability to a new city and a new domain (e.g., the F1 score for requirements in Seattle increases from 77.6% to 93.75% with online learning). After the enhancement from the shield function, CitySpec is now immune to most known textual adversarial inputs (e.g., the attack success rate of DeepWordBug (Gao et al., 2018) after the shield function is reduced to 0% from 82.73%). We test the CitySpec with 18 participants from different domains. CitySpec shows its strong usability and adaptability to different domains, and also its robustness to malicious inputs.     

In‐memory distributed software solution to improve the performance of recommender systems

Many recommender systems are currently available for proposing content (movies, TV series, music, etc.) to users according to different profiling metrics, such as ratings of previously consumed items and ratings of people with similar tastes. Recommendation algorithms are typically executed by powerful servers, as they are computationally expensive. In this paper, we propose a new software solution to improve the performance of recommender systems. Its implementation relies heavily on Apache Spark technology to speed up the computation of recommendation algorithms. It also includes a webserver, an API REST, and a content cache. To prove that our solution is valid and adequate, we have developed a movie recommender system based on two methods, both tested on the freely available Movielens and Netflix datasets. Performance was assessed by calculating root‐mean‐square error values and the times needed to produce a recommendation. We also provide quantitative measures of the speed improvement of the recommendation algorithms when the implementation is supported by a computing cluster. The contribution of this paper lies in the fact that our solution, which improves the performance of competitor recommender systems, is the first proposal combining a webserver, an API REST, a content cache and Apache Spark technology. Copyright © 2016 John Wiley & Sons, Ltd.     

联邦推荐系统的协同过滤冷启动解决方法

基于联邦学习的推荐系统可以在保护用户隐私的情况下,联合多方数据,提升推荐系统的性能,已经成为推荐领域的研究热点之一.联邦协同过滤是联邦推荐系统中最经典及最常用的算法之一.然而,针对联邦协同过滤系统的冷启动问题的研究工作相对较少.针对这一问题,本文提出了一种基于安全内积协议的解决方案.具体地,在系统中添加新用户或新物品时...     

一种检测兴趣漂移的图结构推荐系统

协同过滤是构造推荐系统最有效的方法之一.其中,基于图结构推荐方法成为近来协同过滤的研究热点.基于图结构的方法视用户和项为图的结点,并利用图理论去计算用户和项之间的相似度.尽管人们对图结构推荐系统开展了很多的研究和应用,然而这些研究都认为用户的兴趣是保持不变的,所以不能够根据用户兴趣的相关变化做出合理推荐.本文提出一种新的可以检测用户兴趣漂移的图结构推荐系统.首先,设计了一个新的兴趣漂移检测方法,它可以有效地检测出用户兴趣在何时发生了哪种变化.其次,根据用户的兴趣序列,对评分项进行加权并构造用户特征向量.最后,整合二部投影与随机游走进行项推荐.在标准数据集MovieLens上的测试表明算法优于两个图结构推荐方法和一个评分时间加权的协同过滤方法.