scholarly journals Mobile Context-Aware Systems: Technologies, Resources and Applications

2016 ◽  
Vol 10 (2) ◽  
pp. 25 ◽  
Author(s):  
Alejandro Rivero-Rodriguez ◽  
Paolo Pileggi ◽  
Ossi Antero Nykänen
Keyword(s):  
Personal Information ◽  
Contextual Information ◽  
General Information ◽  
Context Aware ◽  
User Privacy ◽  
Contextual Data ◽  
Potential Applications ◽  
Mobile Context ◽  
Available Information ◽  
Further Development

Mobile applications often adapt their behavior according to user context, however, they are often limited to consider few sources of contextual information, such as user position or language. This article reviews existing work in context-aware systems (CAS), e.g., how to model context, and discusses further development of CAS and its potential applications by looking at available information, methods and technologies. Social Media seems to be an interesting source of personal information when appropriately exploited. In addition, there are many types of general information, ranging from weather and public transport to information of books and museums. These information sources can be combined in previously unexplored ways, enabling the development of smarter mobile services in different domains. Users are, however, reluctant to provide their personal information to applications; therefore, there is a crave for new regulations and systems that allow applications to use such contextual data without compromising the user privacy.

scholarly journals Privacy-preserving recommendations in context-aware mobile environments

2017 ◽  
Vol 25 (1) ◽  
pp. 62-79 ◽  
Author(s):  
Nikolaos Polatidis ◽  
Christos K. Georgiadis ◽  
Elias Pimenidis ◽  
Emmanouil Stiakakis
Keyword(s):  
Recommender Systems ◽  
Privacy Protection ◽  
Personal Information ◽  
Contextual Information ◽  
Context Aware ◽  
User Privacy ◽  
Data Set ◽  
Content Type ◽  
Privacy Concerns ◽  
Usability Tests

Purpose This paper aims to address privacy concerns that arise from the use of mobile recommender systems when processing contextual information relating to the user. Mobile recommender systems aim to solve the information overload problem by recommending products or services to users of Web services on mobile devices, such as smartphones or tablets, at any given point in time and in any possible location. They use recommendation methods, such as collaborative filtering or content-based filtering and use a considerable amount of contextual information to provide relevant recommendations. However, because of privacy concerns, users are not willing to provide the required personal information that would allow their views to be recorded and make these systems usable. Design/methodology/approach This work is focused on user privacy by providing a method for context privacy-preservation and privacy protection at user interface level. Thus, a set of algorithms that are part of the method has been designed with privacy protection in mind, which is done by using realistic dummy parameter creation. To demonstrate the applicability of the method, a relevant context-aware data set has been used to run performance and usability tests. Findings The proposed method has been experimentally evaluated using performance and usability evaluation tests and is shown that with a small decrease in terms of performance, user privacy can be protected. Originality/value This is a novel research paper that proposed a method for protecting the privacy of mobile recommender systems users when context parameters are used.

ConChi

2016 ◽  
pp. 798-820
Author(s):  
Luca Cagliero
Keyword(s):  
Contextual Information ◽  
Extraction Process ◽  
Mobile User ◽  
Mobile Service ◽  
Context Aware ◽  
Time Periods ◽  
Context Aware System ◽  
Mobile Context ◽  
High Level ◽  
Abstraction Levels

Mobile context-aware systems focus on adapting mobile service provisions to the actual user needs. They offer personalized services based on the context in which mobile users' requests have been submitted. Since contextual information changes over time, the application of established itemset change mining algorithms to context-aware data is an appealing research issue. Change itemset discovery focuses on discovering patterns which represent the temporal evolution of frequent itemsets in consecutive time periods. However, the sparseness of the analyzed data may bias the extraction process, because itemsets are likely to become infrequent at certain time periods. This chapter presents ConChI, a novel context-aware system that performs change itemset mining from context-aware data with the aim at supporting mobile expert decisions. To counteract data sparseness itemset change mining is driven by an analyst-provided taxonomy which allows analyzing data correlation changes at different abstraction levels. In particular, taxonomy is exploited to represent the knowledge that becomes infrequent in certain time periods by means of high level (generalized) itemsets. Experiments performed on real contextual data coming from a mobile application show the effectiveness of the proposed system in supporting mobile user and service profiling.

Dynamic Control Mechanisms for User Privacy Enhancement

Cyber Crime ◽  
2013 ◽  
pp. 534-556
Author(s):  
Amr Ali Eldin
Keyword(s):  
Decision Making ◽  
Private Information ◽  
Contextual Information ◽  
Dynamic Control ◽  
Control Mechanisms ◽  
Context Aware ◽  
User Privacy ◽  
Privacy Concerns ◽  
Fuzzy Logic Approach ◽  
Expected Benefits

Despite the expected benefits behind context-awareness and the need for developing more and more context-aware applications, we enunciate that privacy represents a major challenge for the success and widespread adoption of these services. This is due to the collection of huge amount of users’ contextual information, which would highly threaten their privacy concerns. Controlling users’ information collection represents a logical way to let users get more acquainted with these context-aware services. Additionally, this control requires users to be able to make consent decisions which face a high degree of uncertainty due to the nature of this environment and the lack of experience from the user side with information collectors’ privacy policies. Therefore, intelligent techniques are required in order to deal with this uncertainty. In this chapter, the auhtors propose a consent decision-making mechanism, ShEM, which allows users to exert automatic and manual control over their private information. An enhanced fuzzy logic approach was developed for the automatic decision making process. The proposed mechanism has been prototyped and integrated in a UMTS location-based services testbed on a university campus. Users have experienced the services in real time. A survey of users’ responses on the privacy functionality has been carried out and analyzed as well. Users’ response on the privacy functionality was positive. Additionally, results obtained showed that a combination of both manual and automatic privacy control modes in one approach is more likely to be accepted than only a complete automatic or a complete manual privacy control.

ConChi

2014 ◽  
pp. 263-285
Author(s):  
Luca Cagliero
Keyword(s):  
Contextual Information ◽  
Extraction Process ◽  
Mobile User ◽  
Mobile Service ◽  
Context Aware ◽  
Time Periods ◽  
Context Aware System ◽  
Mobile Context ◽  
High Level ◽  
Abstraction Levels

Mobile context-aware systems focus on adapting mobile service provisions to the actual user needs. They offer personalized services based on the context in which mobile users’ requests have been submitted. Since contextual information changes over time, the application of established itemset change mining algorithms to context-aware data is an appealing research issue. Change itemset discovery focuses on discovering patterns which represent the temporal evolution of frequent itemsets in consecutive time periods. However, the sparseness of the analyzed data may bias the extraction process, because itemsets are likely to become infrequent at certain time periods. This chapter presents ConChI, a novel context-aware system that performs change itemset mining from context-aware data with the aim at supporting mobile expert decisions. To counteract data sparseness itemset change mining is driven by an analyst-provided taxonomy which allows analyzing data correlation changes at different abstraction levels. In particular, taxonomy is exploited to represent the knowledge that becomes infrequent in certain time periods by means of high level (generalized) itemsets. Experiments performed on real contextual data coming from a mobile application show the effectiveness of the proposed system in supporting mobile user and service profiling.

A SURVEY OF CONTEXT-AWARE MOBILE RECOMMENDATIONS

2013 ◽  
Vol 12 (01) ◽  
pp. 139-172 ◽  
Author(s):  
QI LIU ◽  
HAIPING MA ◽  
ENHONG CHEN ◽  
HUI XIONG
Keyword(s):  
Recommender Systems ◽  
User Interfaces ◽  
State Of The Art ◽  
Contextual Information ◽  
General Notion ◽  
Context Aware ◽  
Key Issues ◽  
Privacy Problem ◽  
Mobile Context ◽  
The Right

Mobile recommender systems target on recommending the right product or information to the right mobile users at anytime and anywhere. It is well known that the contextual information is often the key for the performances of mobile recommendations. Therefore, in this paper, we provide a focused survey of the recent development of context-aware mobile recommendations. After briefly reviewing the state-of-the-art of recommender systems, we first discuss the general notion of mobile context and how the contextual information is collected. Then, we introduce the existing approaches to exploit contextual information for modeling mobile recommendations. Furthermore, we summarize several existing recommendation tasks in the mobile scenarios, such as the recommendations in the tourism domain. Finally, we discuss some key issues that are still critical in the field of context-aware mobile recommendations, including the privacy problem, the energy efficiency issues, and the design of user interfaces.

Dynamic Control Mechanisms for User Privacy Enhancement

2011 ◽  
pp. 115-137
Author(s):  
Amr Ali Eldin
Keyword(s):  
Decision Making ◽  
Private Information ◽  
Contextual Information ◽  
Dynamic Control ◽  
Control Mechanisms ◽  
Context Aware ◽  
User Privacy ◽  
Privacy Concerns ◽  
Fuzzy Logic Approach ◽  
Expected Benefits

Despite the expected benefits behind context-awareness and the need for developing more and more context-aware applications, we enunciate that privacy represents a major challenge for the success and widespread adoption of these services. This is due to the collection of huge amount of users’ contextual information, which would highly threaten their privacy concerns. Controlling users’ information collection represents a logical way to let users get more acquainted with these context-aware services. Additionally, this control requires users to be able to make consent decisions which face a high degree of uncertainty due to the nature of this environment and the lack of experience from the user side with information collectors’ privacy policies. Therefore, intelligent techniques are required in order to deal with this uncertainty. In this chapter, the auhtors propose a consent decision-making mechanism, ShEM, which allows users to exert automatic and manual control over their private information. An enhanced fuzzy logic approach was developed for the automatic decision making process. The proposed mechanism has been prototyped and integrated in a UMTS location-based services testbed on a university campus. Users have experienced the services in real time. A survey of users’ responses on the privacy functionality has been carried out and analyzed as well. Users’ response on the privacy functionality was positive. Additionally, results obtained showed that a combination of both manual and automatic privacy control modes in one approach is more likely to be accepted than only a complete automatic or a complete manual privacy control.

Privacy and Trust Issues in Context-Aware Pervasive Computing

2010 ◽  
pp. 352-377 ◽  
Author(s):  
Pierre E. Abi-Char ◽  
Bachar El-Hassan ◽  
Mounir Mokhtari
Keyword(s):  
Pervasive Computing ◽  
Context Awareness ◽  
Contextual Information ◽  
Mobile Technologies ◽  
Security Requirements ◽  
Security And Privacy ◽  
Context Aware ◽  
User Privacy ◽  
Privacy And Security ◽  
Information And Communication

The growing evolution of information and communication technology (ICT) systems towards more pervasive and ubiquitous infrastructures contribute significantly to the deployment of services anywhere, at anytime and for anyone. To provide personalized services in such infrastructures, we should consider both user’s privacy and security requirements and context-awareness environment. This can be really achieved owing to context awareness systems which allow us to benefit from sensing and mobile technologies to derive more accurate data about the user and his/her location. While the availability of contextual information may introduce new threats against security and privacy, it can also be used to improve dynamic, adaptive and autonomic aspects of security, and user privacy. Moreover, context-aware information offers new opportunities for the establishment of trust relationship among involved entities (e.g., users, devices, and platforms). As context awareness represents new challenges and new opportunities regarding privacy, trust and security of users in pervasive computing environments (PCE), the main purpose of this chapter aims to survey each of the involved issues to understand and address the interdependencies among them.

Improving personalised query reformulation with embeddings

2021 ◽  
pp. 016555152096869
Author(s):  
Xiaojuan Zhang
Keyword(s):  
Information Needs ◽  
Graphical Model ◽  
Personal Information ◽  
Contextual Information ◽  
User Preferences ◽  
Query Reformulation ◽  
Context Aware ◽  
Current State ◽  
Proposed Model ◽  
Historical Queries

As a mechanism to guide users towards a better representation of their information needs, the query reformulation method generates new queries based on users’ historical queries. To preserve the original search intent, query reformulations should be context-aware and should attempt to meet users’ personal information needs. The mainstream method aims to generate candidate queries first, according to their past frequencies, and then score (re-rank) these candidates based on the semantic consistency of terms, dependency among latent semantic topics and user preferences. We exploit embeddings (i.e. term, user and topic embeddings) to use contextual information and individual preferences more effectively to improve personalised query reformulation. Our work involves two major tasks. In the first task, candidate queries are generated from an original query by substituting or adding one term, and the contextual similarities between the terms are calculated based on the term embeddings and augmented with user personalisation. In the second task, the candidate queries generated in the first task are evaluated and scored (re-ranked) according to the consistency of the semantic meaning of the candidate query and the user preferences based on a graphical model with the term, user and topic embeddings. Experiments show that our proposed model yields significant improvements compared with the current state-of-the-art methods.

scholarly journals Organic magnetoelectric and optomagnetic couplings: perspectives for organic spin optoelectronics

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Zhongxuan Wang ◽  
Wei Qin
Keyword(s):  
Ferromagnetic Materials ◽  
The Past ◽  
Potential Applications ◽  
Further Development

AbstractOver the past years, the development of organic ferromagnetic materials has been investigated worldwide for potential applications. Due to the couplings among the charge, orbit, spin, and phonon in organic ferromagnetic materials, magnetoelectric, and optomagnetic couplings have been realized and observed. In this review, progress in organic magnetoelectric and optomagnetic couplings is presented, and the mechanisms behind the phenomena are also briefly summarized. Hopefully, the understanding of magnetoelectric and optomagnetic couplings could provide guidance for the further development of organic spin optoelectronics.

scholarly journals Context-Aware Neural Machine Translation for Korean Honorific Expressions

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1589
Author(s):  
Yongkeun Hwang ◽  
Yanghoon Kim ◽  
Kyomin Jung
Keyword(s):  
Machine Translation ◽  
Deep Neural Networks ◽  
Contextual Information ◽  
Context Aware ◽  
Neural Machine Translation ◽  
Translation Quality ◽  
Sentence Level ◽  
Proposed Model ◽  
The Given ◽  
The Relationship

Neural machine translation (NMT) is one of the text generation tasks which has achieved significant improvement with the rise of deep neural networks. However, language-specific problems such as handling the translation of honorifics received little attention. In this paper, we propose a context-aware NMT to promote translation improvements of Korean honorifics. By exploiting the information such as the relationship between speakers from the surrounding sentences, our proposed model effectively manages the use of honorific expressions. Specifically, we utilize a novel encoder architecture that can represent the contextual information of the given input sentences. Furthermore, a context-aware post-editing (CAPE) technique is adopted to refine a set of inconsistent sentence-level honorific translations. To demonstrate the efficacy of the proposed method, honorific-labeled test data is required. Thus, we also design a heuristic that labels Korean sentences to distinguish between honorific and non-honorific styles. Experimental results show that our proposed method outperforms sentence-level NMT baselines both in overall translation quality and honorific translations.

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