Search your memory ! - an associative memory based desktop search system

Recent advances in mobile devices and their sensing capabilities have enabled the collection of rich contextual information and mobile device usage records through the device logs. These context-rich logs open a venue for mining the personal preferences of mobile users under varying contexts and thus enabling the development of personalized context-aware recommendation and other related services, such as mobile online advertising. In this article, we illustrate how to extract personal context-aware preferences from the context-rich device logs, or context logs for short, and exploit these identified preferences for building personalized context-aware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his or her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences based on two different assumptions, namely, context-independent and context-dependent assumptions, which can fit into different application scenarios. Finally, extensive experiments on a real-world dataset show that both approaches are effective and outperform baselines with respect to mining personal context-aware preferences for mobile users.

Typical tag recommendation systems for photos shared on social networks such as Flickr, use visual content analysis, collaborative filtering or personalization strategies to produce annotations. However, the dependence on manual intervention and the knowledge of sufficient personal preferences coupled with the folksonomic issues limit the scope of these strategies. In this paper, we present a fully automatic and folksonomically scalable tag recommendation model that can recommend tags for a user's photos without an explicit knowledge of the user's personal tagging preferences. The model is learned using the collective tagging behavior of other users in the user's local interaction network, which we believe approximates the user's preferences, at least partially. The tag recommendation model generates content-based annotations and then uses a Naive Bayes formulation to translate these annotations to a set of folksonomic tags selected from the tags used by the users in the local interaction network. Quantitative and qualitative comparisons with 890 Flickr networks show that this approach is highly useful for tag recommendation in the presence of insufficient information of a user's own preferences.

In this paper, we introduce MyCampus, a Semantic Web environment for context-aware mobile services, which we are in the process of developing and validating on Carnegie Mellon University's campus. The environment revolves around a growing collection of customizable agents capable of (semi-) automatically discovering and accessing user personal resources (e.g. calendar, location tracking functionality, food preferences) and Web services as they assist their users in carrying out different tasks such as planning an evening out, organizing a study group or filtering incoming messages. The openness of the MyCampus architecture directly derives from a set of ontologies for describing personal resources, contextual attributes, user preferences and web services. Contextual information and other personal details about a user are accessed via a Semantic e-Wallet, subject to access privileges set by the user.

People retain more new verbal episodic information for at least 7 days if they rest for a few minutes after learning than if they attend to new information. It is hypothesized that rest allows for superior consolidation of new memories. In rodents, rest periods promote hippocampal replay of a recently travelled route, and this replay is thought to be critical for memory consolidation and subsequent spatial navigation. If rest boosts human memory by promoting hippocampal replay/consolidation, then the beneficial effect of rest should extend to complex (hippocampal) memory tasks, for example, tasks probing associations and sequences. We investigated this question via a virtual reality route memory task. Healthy young participants learned two routes to a 100% criterion. One route was followed by a 10-min rest and the other by a 10-min spot the difference game. For each learned route, participants performed four delayed spatial memory tests probing: (i) associative (landmark-direction) memory, (ii) cognitive map formation, (iii) temporal (landmark) order memory, and (iv) route memory. Tests were repeated after 7 days to determine any long-term effects. No effect of rest was detected in the route memory or cognitive map tests, most likely due to ceiling and floor effects, respectively. Rest did, however, boost retention in the associative memory and temporal order memory tests, and this boost remained for at least 7 days. We therefore demonstrate that the benefit of rest extends to (spatial) associative and temporal order memory in humans. We hypothesise that rest allows superior consolidation/hippocampal replay of novel information pertaining to a recently learned route, thus boosting new memories over the long term.

Recommendation System has become more customized than ever. However, it suffers from cold start and data sparsity issues. Cross Domain Recommendation (CDR) has been proposed as a promising and feasible approach which yields recommendations in the destination domain based on the characteristic and personalized preferences of the users in the source domain. Since the fondness varies for each user, their taste bridge would also be different. Most of the well-known existing models make use of common preference bridge when it comes to transferring preferences for all the users in the system. Personalized Preference Transfer (PPT) for personalizing user preferences using Characteristic Encoder (CE) where user characteristic is mapped to initializing maps is pro-posed for cold start users and from books for warm start users. In this model, a Meta net-work with user attributes is used to attain the bridge preferences for individual users. The Deep Neural Network (DNN) which is a neural network model is used to provide optimized recommendations to the users. The accuracy and efficiency of personalized user preferences transfer is determined based on the comparisons from different models and train/test ratios, making use of two evaluation metrics MAE (Mean Absolute Error) and RMSD (Root Mean Squared Deviation).

The aim of personalizing web information systems is to tailor content (media) to the user's personal preferences, goals and context, in turn increasing the reusability of that content. However, most developers are increasingly seeking to apply 'Web as a Platform' based approaches where web-based content is integrated with web services to provide the platform and environment features that were traditionally offered by desktop environments and applications. Therefore, next generation personalized web information systems need to be capable of dynamically personalizing, adapting and integrating web content and web services to create customized experiences for individual users. The goal of the research presented in this paper is to provide innovative techniques and technologies to achieve this. As part of this research, a novel architecture for the generation of Personalized Learning Activities, which highlights both the need for, and the power of combining and personalizing content and services has been developed. The system has been evaluated in the context of a Higher Education use case based on the user's perception of three key elements of the Personalized Learning Activity: the personalization, the usability and the educational benefits.

In this paper we present a Japanese-English transfer- based machine translation system. Our main research contribution is that the transfer rules are not handcrafted but are learnt automatically from a parallel corpus. We learn specific transfer rules from sentence pairs, which are then generalized in a consolidation phase to avoid overtraining. The system has been implemented in Amzi! Prolog, which offers scalability for large rule bases, full Unicode support for Japanese characters, and several APIs for the seamless integration of the translation functionality into common office environments, e.g. Microsoft Word. The dynamic nature of our system allows for an easy customization of the rule base according to the user's personal preferences.

In many of today's software systems, most notably online social networks, users can share personal information. Behind the simple action of sharing is a more complicated thought process regarding privacy: which content to share, with whom to share, and why to share. For a user, it's time-consuming and error-prone to check individual personal content for privacy violations. Hence, it would be ideal if a personal assistant can learn its users' privacy preferences and subsequently help users' decision-making by signaling potentially private content. A personalized privacy assistant can help its user make privacy decisions taking into account the ambiguity and uncertainty of privacy predictions as well as its user's personal preferences. Moreover, an explanation of why an image is considered public or private can aid the user in understanding the assistant's decisions.

In order to identify a user's personal preference in navigating the Web or to recommend collected Web information, it is very useful to analyze the user's Web-browsing behavior. However, it is difficult to determine which Web-browsing behaviors are influential on predicting a user's interest because each individual has his/her own habit and personal manner in surfing the Web and locating documents of interest. In this study, we propose an adaptive personalized recommender system based on a preference-thesaurus constructed by learning a user's Web-browsing behavior. The major components of the proposed recommender system are the Web-browsing behavior monitor, preference-thesaurus constructor, relevant document recommender, and user feedback learner. The adaptive nature of the proposed system allows the personalization of recommendation and the identification of the ever-changing influential Web-browsing behaviors. A proof-of-concept system is implemented and experiments are performed to verify the system's capability to personalize recommendation and to learn through user's feedbacks.

In this paper, we discuss how a short-range wireless communication scheme called Mobile Encounter Network (MEN) can provide additional value for both the consumer and the service/product provider (the company). When used as an information search system on the market environment, a MEN-based system allows services and products being promoted at the location they are available. From the customer's point of view, MEN may provide a digitally augmented vision, an enhanced view to the current environment. Combined with a set of filtering and search rules this may provide a self-manageable context combining the user's own personal environment and preferences to the services and features available in the current surroundings. An imaginary case study of this kind of system in execution is provided. The conclusion is that such method may can generate additional revenue to the company and please the customers' buying process.

Measuring Central Human Capabilities in Transport As a Complementary Method for Appraising Transport Projects in the Context of Santiago De Chile

Despite being a challenging research field with many unresolved problems, recommender systems are getting more popular in recent years. These systems rely on the personal preferences of users on items given in the form of ratings and return the preferable items based on choices of like-minded users. In this study, a graph-based recommender system using link prediction techniques incorporating similarity metrics is proposed. A graph-based recommender system that has ratings of users on items can be represented as a bipartite graph, where vertices correspond to users and items and edges to ratings. Recommendation generation in a bipartite graph is a link prediction problem. In current literature, modified link prediction approaches are used to distinguish between fundamental relational dualities of like vs. dislike and similar vs. dissimilar. However, the similarity relationship between users/items is mostly disregarded in the complex domain. The proposed model utilizes user-user and item-item cosine similarity value with the relational dualities in order to improve coverage and hits rate of the system by carefully incorporating similarities. On the standard MovieLens Hetrec and MovieLens datasets, the proposed similarity-inclusive link prediction method performed empirically well compared to other methods operating in the complex domain. The experimental results show that the proposed recommender system can be a plausible alternative to overcome the deficiencies in recommender systems.

Worries about the increasing number of Sybils in online social networks (OSNs) are amplified by a range of security issues; thus, Sybil detection has become an urgent real-world problem. Lightweight and limited data-friendly, LBP (Loopy Belief Propagation)-based Sybil-detection methods on the social graph are extensively adopted. However, existing LBP-based methods that do not utilize node attributes often assume a global or predefined homophily strength of edges in the social graph, while different user’s discrimination and preferences may vary, resulting in local homogeneity differences. Another issue is that the existing message-passing paradigm uses the same edge potential when propagating belief to both sides of a directed edge, which does not agree with the trust interaction in one-way social relationships. To bridge these gaps, we present SybilHP, a Sybil-detection method optimized for directed social networks with adaptive homophily prediction. Specifically, we incorporate an iteratively updated edge homophily estimation into the belief propagation to better adapt to the personal preferences of real-world social network users. Moreover, we endow message passing on edges with directionality by a direction-sensitive potential function design. As a result, SybilHP can better capture the local homophily and direction pattern in real-world social networks. Experiments show that SybilHP works with high detection accuracy on synthesized and real-world social graphs. Compared with various state-of-the-art graph-based methods on a large-scale Twitter dataset, SybilHP substantially outperforms existing methods.

Shared mobility can fundamentally change road transport and improve life quality. Car-sharing services have reached a high level of growth worldwide and members adopting them as an alternative transportation mode are constantly on the increase. The advent of highly automated vehicles could boost the adoption of car sharing as more inexpensive and convenient on-demand services could be achieved. In recent years, vehicle automation is facilitated and supported by the latest advances in information and communication technologies, forming the cornerstone of the new generation of In-Vehicle Intelligent Systems. The goal of this study is to introduce a novel in-vehicle cognitive framework that utilises (i) driver's/user's profile data and personal preferences, (ii) real-time information associated with the vehicle's driving environment, and (iii) previous knowledge and experience, in proposing adaptations of the vehicle's level of autonomy, in an automated manner, when a driver/user wishes to make a road journey within the automated vehicle sharing context. Knowledge is obtained through the exploitation of Bayesian networking principles. Indicative results on the performance of the proposed cognitive mechanism and the associated knowledge-based selection scheme are presented, mostly with regards to proactively identifying the optimum level of autonomy to be implemented.

Virtual-reality-based interactions with virtual agents (VAs) are likely subject to similar influences as human-human interactions. In either real or virtual social interactions, interactants try to maintain their personal space (PS), an ubiquitous, situative, flexible safety zone. Building upon larger PS preferences to humans and VAs with angry facial expressions, we extend the investigations to whole-body emotional expressions. In two immersive settings-HMD and CAVE-66 males were approached by an either happy, angry, or neutral male VA. Subjects preferred a larger PS to the angry VA when being able to stop him at their convenience (Sample task), replicating previous findings, and when being able to actively avoid him (Pass By task). In the latter task, we also observed larger distances in the CAVE than in the HMD.