Toward personalized and adaptive QoS assessments via context awareness

Quality of Service (QoS) properties play an important role in distinguishing between functionally equivalent services and accommodating the different expectations of users. However, the subjective nature of some properties and the dynamic and unreliable nature of service environments may result in cases where the quality values advertised by the service provider are either missing or untrustworthy. To tackle this, a number of QoS estimation approaches have been proposed, using the observation history available on a service to predict its performance. Although the context underlying such previous observations (and corresponding to both user and service related factors) could provide an important source of information for the QoS estimation process, it has only been used to a limited extent by existing approaches. In response, we propose a context‐aware quality learning model, realized via a learning‐enabled service agent, exploiting the contextual characteristics of the domain to provide more personalized, accurate, and relevant quality estimations for the situation at hand. The experiments conducted demonstrate the effectiveness of the proposed approach, showing promising results (in terms of prediction accuracy) in different types of changing service environments.     

基于Web使用挖掘的个性化学习推荐系统

针对当前E learning推荐系统存在的问题，引入多Agent（MAS）系统，提出一种基于Web使用挖掘的集成MAS与Web services的分布式智能推荐系统模型。该模型能动态生成基于用户使用信息的个性化链接页面，有效地帮助学员找到所需的资源信息。提出了一种基于最近最少使用策略的系统推荐算法，包括系统整体实现算法、系统聚类算法及推荐算法。实时性能分析显示该系统运行性能良好。     

Ontology based automatic feature recognition framework

AFR has long been realized as a key technology for design automation. A significant shortcoming in AFR is that most of them are individual systems that are isolated from each other, due to the absence of a standard feature library or feature modeling techniques. Few studies attempted to overcome this problem by allowing a certain degree of user customization or extension, which are still far from success. In order to address this issue, this paper proposes an ontology-based feature recognition framework. In the framework, features are captured transparently and hierarchically within a formal OWL ontology, and the feature recognition is achieved by applying an efficient backward-chained ontology reasoner to reason through the ontology. The resulting feature recognition system shows a high level of flexibility, maintainability, and explainability, for both representing and recognizing features. The effectiveness of the framework is finally demonstrated with three case studies.     

基于上下文感知移动中间件的研究与设计

移动环境和设备的限制,给移动开发带来了挑战。移动中间件能够为移动应用开发提供了一个很好的基础平台。叙述了自适应机制在中间件层实现的必要性,分析了传统面向对象中间件应用在移动计算环境中的不足。根据移动应用的需求,提出了一个基于上下文感知的移动中间件体系结构。     

A personalized IPTV channel-recommendation mechanism based on the MapReduce framework

Internet protocol television viewers spend considerable time browsing through the many existing channels, which is inefficient and time consuming. Although the recommendation system can solve the channel-switching problem, its performance is slow unless it is adapted to read a large amount of data sets. This study proposes a novel cloud-assisted channel-recommendation system under a cloud computing environment, channel association rules (CARs), to speed up the performance of channel switching, thereby help users to find their favorite channels in less time. The CARs algorithm is compared with the conventional (COV) solution and the most frequently selected (MFS) algorithm based on MovieLens data sets. The experimental results indicate that the predictive accuracy of CARs is superior to that of the COV and MFS algorithms. In addition, CARs use parallel computing in MapReduce to distribute large amounts of user history logs across multiple computers for processing. The experimental results show that the proposed algorithm can be employed to efficiently handle big data in a finite time when a huge of cloud servers are rented from commercial cloud providers such as Amazon Elastic Compute Cloud (EC2), Microsoft HDinsight.     

自适应过滤算法在社区E-learning的个性化服务系统中的研究

针对学习型社区中的教育需求,在传统算法上加以改进,提出了一种基于向量空间模型的教育资源自适应过滤算法.通过训练算法,提取特征向量和伪反馈建立初始模板,设置初始阈值.然后通过过滤算法根据用户的反馈信息自适应地调整模板和阈值.该算法在执行过程中,不需要大量的初始文本,同时在过滤的过程中可不断地进行自主学习来提高过滤精度.该算法已在个性化知识服务系统中进行验证,结果表明是有效的.     

Adaptive personalized recommendation based on adaptive learning

Collaborative filtering has been widely applied in many fields in recent years due to the increase in web-based activities such as e-commerce and online content distribution. Current collaborative filtering techniques such as correlation-based, SVD-based and supervised learning-based approaches provide good accuracy, but are computationally very expensive and can only be deployed in static off-line settings, where the known rating information does not change with time. However, a number of practical scenarios require dynamic adaptive collaborative filtering that can allow new users, items and ratings to enter the system at a rapid rate. In this paper, we consider a novel adaptive personalized recommendation based on adaptive learning. Fast adaptive learning runs through all the aspects of the proposed approach, including training, prediction and updating. Empirical evaluation of our approach on Movielens dataset demonstrates that it is possible to obtain accuracy comparable to that of the correlation-based, SVD-based and supervised learning-based approaches at a much lower computational cost.     

地理位置和时间感知的表示学习框架

现有时空感知的表示学习框架无法对强时空语义的实际场景存在的“When”、“Where”和“What”3个问题给出一个统一的解决方案。同时,现有的时间和空间建模上的研究方案也存在着一定的缺陷,无法在复杂的实际场景中取得最优的性能。为了解决这些问题,本文提出了一个统一的用户表示框架—GTRL (geography and time aware representation learning),可以同时在时间和空间的维度上对用户的历史行为轨迹进行联合建模。在时间建模上,GTRL采用函数式的时间编码以及连续时间和上下文感知的图注意力网络,在动态的用户行为图上灵活地捕获高阶的结构化时序信息。在空间建模上,GTRL采用了层级化的地理编码和深度历史轨迹建模模块高效地刻画了用户的地理位置偏好。GTRL设计了统一的联合优化方案,同时在交互预测、交互时间预测以及交互位置3个任务上进行模型学习。最后,本文在公开数据集和工业数据集上设计了大量的实验,分别验证了GTRL相较学术界基线模型的优势,以及在实际业务场景中的有效性。     

ORACON: An adaptive model for context prediction

Context prediction has been receiving considerable attention in the last years. This research area seems to be the next logical step in context-aware computing, which, until a few years ago, had been concerned more with the present and the past temporal dimensions. Most of research works related to context prediction employ the same algorithm for all cases. We did not find any approach that automatically decides the best prediction method according to the situation. Therefore, we propose the ORACON model. ORACON adapts itself in order to apply the best algorithm to the case. This adaptive behavior is the main contribution of this work and differentiates the proposed model of other related works. Furthermore, ORACON supports other important aspects of ubiquitous computing, such as, context formal representation and privacy. We have built a functional prototype that allowed us to conduct two experiments. The first experiment successfully tested the main functionalities provided by ORACON to support context prediction and privacy aspects. The test used context histories generated with a location database that contains 22 millions chekins across 220,000 users in the location sharing services Foursquare and Twitter. The second experiment assessed the adaptive feature of the ORACON. The test simulated the behavior of 30 users for a period of 30 days, using context histories generated through the Siafu simulator. This tool generates data for the evaluation and the comparison of machine learning methods in mobile context-aware settings. We concluded that ORACON chose the most accurate prediction algorithm in the simulated scenario, proving that the model reached the main contribution sought by this research.     

基于情境感知技术的智慧城市服务系统研究

随着城市规模越来越复杂,全国各级政府都在进行城市物联网和信息化建设,目前虽然搭建了互联骨干网和部署了大量的传感器,收集了众多的城市行为数据,但落后的信息管理模式难以体现信息价值,将信息体现在服务提升之中,造成信息资源的极大浪费。因此,将情境感知技术引入到智慧城市服务的应用之中,构建了一个基于情境感知的城市服务系统,并通过情境信息采集、情境信息推理和服务配置模型等关键技术,实时感知城市内的情境需求,从而提供智能化的业务服务组合。最后通过一社区智慧街道管理系统来验证本文所设计的系统效果。     

An adaptive user interface based on personalized learning

This adaptive user interface provides individualized, just-in-time assistance to users by recording user interface events and frequencies, organizing them into episodes, and automatically deriving patterns. It also builds, maintains, and makes suggestions based on user profiles.     

An enhanced model framework of personalized material flow services

A growing concern about the consumer behavior in Internet economy has spurred the study of Material Flow, resulting in a unique type of consumer behavioral analysis. This research proposes an enhanced conceptual model for Personalized Material Flow Services for consumer behavior. In the era of Internet information technology, customer’s taste tends to be personalized for their market demand. It is observed that there are number of “Long Tail” phenomena in several successful e-commerce business cases. However, the Long Tail phenomenon is an open question for our research in terms of its role in e-commerce marketing. In the proposed model-X-Party Personalized Material Flow Services, three elements are discussed. They are “providing”, “locating” and “obtaining” based on X-Party Material Flow theory for which the concept of virtual collector, information filter and Material Flow coordinator are discussed. Business examples of Amazon, Dangdang and Taobao are used to analyze the elements of the virtual collector, information filter and Material Flow coordinator of the Personalized Material Flow Service system.     

A hybrid E-learning recommendation integrating adaptive profiling and sentiment analysis

This research proposes a novel framework named Enhanced e-Learning Hybrid Recommender System (ELHRS) that provides an appropriate e-content with the highest predicted ratings corresponding to the learner’s particular needs. To accomplish this, a new model is developed to deduce the Semantic Learner Profile automatically. It adaptively associates the learning patterns and rules depending on the learner’s behavior and the semantic relations computed in the semantic matrix that mutually links e-learning materials and terms. Here, a semantic-based approach for term expansion is introduced using DBpedia and WordNet ontologies. Further, various sentiment analysis models are proposed and incorporated as a part of the recommender system to predict ratings of e-learning resources from posted text reviews utilizing fine-grained sentiment classification on five discrete classes. Qualitative Natural Language Processing (NLP) methods with tailored-made Convolutional Neural Network (CNN) are developed and evaluated on our customized dataset collected for a specific domain and a public dataset. Two improved language models are introduced depending on Skip-Gram (S-G) and Continuous Bag of Words (CBOW) techniques. In addition, a robust language model based on hybridization of these couple of methods is developed to derive better vocabulary representation, yielding better accuracy 89.1% for the CNN-Three-Channel-Concatenation model. The suggested recommendation methodology depends on the learner’s preferences, other similar learners’ experience and background, deriving their opinions from the reviews towards the best learning resources. This assists the learners in finding the desired e-content at the proper time.     

An evaluation framework for energy aware buildings using statistical model checking

Cyber-physical systems are to be found in numerous applications throughout society.The principal barrier to develop trustworthy cyber-physical systems is the lack of expressive modelling and specification formalisms supported by efficient tools and methodologies.To overcome this barrier,we extend in this paper the modelling formalism of the tool UPPAAL-SMC to stochastic hybrid automata,thus providing the expressive power required for modelling complex cyber-physical systems.The application of Statistical Model Checking provides a highly scalable technique for analyzing performance properties of this formalisms.A particular kind of cyber-physical systems are Smart Grids which together with Intelligent,Energy Aware Buildings will play a major role in achieving an energy efficient society of the future.In this paper we present a framework in UPPAAL-SMC for energy aware buildings allowing to evaluate the performance of proposed control strategies in terms of their induced comfort and energy profiles under varying environmental settings(e.g.weather,user behavior etc.).To demonstrate the intended use and usefulness of our framework,we present an application to the Hybrid Systems Verification Benchmark.     

浅析基于用户认知语境的个性化导航研究

现代网络的快速发展,使网络上出现了大量的不同形式的信息内容,大量的信息内容通过不同的形式呈现。因此,需要用户自身的习惯偏好和知识环境在信息导航系统中发挥相应的主要作用。从用户个体认知语境作为出发点,通过导航实例对问题进行了深入研究,希望对相关工作人员的工作能够有所帮助。     

Context-aware system for proactive personalized service based on context history

Predicting the preferences of users and providing the personalized services or products based on their preferences are the important issues. However, the research considering users’ preferences on context-aware computing is a relatively insufficient research field. Hence, this paper aims to propose an agent-based framework for providing the personalized services using context history on context-aware computing. Based on the proposed framework, we implement a prototype system to show the feasibility of the framework. Previous researches require that the users input their preference manually, but this research provides the personalized services extracting the relationship between users’ profile and services under the same context automatically.     

基于领域本体的电子学习资源库模型

随着电子学习系统快速的发展,电子学习资源呈现爆炸式的增长,如何有效地组织海量电子学习资源成为构建高效电子学习系统的重要因素。针对现有资源库在资源组织方面存在的不足,提出了一个基于领域知识本体的电子学习资源库检索模型,该模型利用领域知识来构建领域知识本体库并通过抽取电子学习资源元数据构建元数据库,通过映射关系完成对电子学习资源的语义组织,并在此基础之上构建一个语义检索模型,以有效地解决现有电子学习资源检索中丢失语义背景的问题,使检索结果在查全率、查准率方面有所提高,更加符合用户的需求。     

Context aware based user customized light therapy service using security framework

Current IT market has increased significantly boosted by the development of network technology and IT technology. As we move into an aging society, global people have paid attentions to the matters related to health while the many technologies related to health have been developed, too. Such a change of social paradigm has improved the quality of life but various mental diseases show its growing trend. The past mental disease matters were limited to the area of chronic mental disease patients, but recently light mental diseases including the stress caused by excessive work and internet addiction are included in this area. Especially there are many cases where early treatable depressions have been developed into the serious diseases as they had no chance to recognition and healing or missed the appropriate time for healing. Even if various health related mobile services to solve this show it gradual growing trend now, providing services which correspond to the needs of users, are difficult in the actual situation, while many researches are performed to provide the customized service by utilizing PHR but they are very vulnerable in the aspect of security. Therefore, in this paper, we proposed the context aware based user customized light therapy service using security framework. The proposed service applied the context aware based security framework to enhance the security of health service for PHR interlocking, which can protect the medical information by complying with standard based encryption, security guideline. For this purpose, we arranged that object group support component of DOGF should be existed in server system and components such as user information and distributed sources re-composed by smart health for service providing. Based on this, the user’s states was analyzed in real time through Personal Health Record and Galvanic Skin Response of users, the brightness and chromaticity of bulb was adjusted depending on the states of user for soothing effect, the real-time states was figured out through questionnaires for mental states analysis of smartphone application to provide the real-time treatment for users stress alleviating and concentration enhancement. Also focused on Psychological illness, we arranged to provide the real-time light therapy according to the states of users based on the data of patients collected from smart device in order to mental disease prevention and treatment caused from the stress of workers who work with computer for a long time such as office workers, program developer and home workers.