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Session 8B: SS2: Modeling Multimedia Behaviors
For more details of this session, please visit: http://mmm2017.ru.is/index.php/special-session-2-modeling-multimedia-behaviors/.
1:00pm - 1:20pm
Demographic Attribute Inference from Social Multimedia Behaviors: a Cross-OSN Approach
1National Lab of Pattern Recognition, Institute of Automation, CAS, China, People's Republic of; 2University of Chinese Academy of Sciences, Beijing 100049, China
This study focuses on exploiting the dynamic social multimedia behaviors to infer the stable demographic attributes. Existing demographic attribute inference studies are devoted to developing advanced features/models or exploiting external information and knowledge. The conflicts between dynamicity of behaviors and the steadiness of demographic attributes are largely ignored. To address this issue, we introduce a cross-OSN approach to discover the shared stable patterns from users’ social multimedia behaviors on multiple Online Social Net- works (OSNs). The basic assumption for the proposed approach is that, the same user’s cross-OSN behaviors are the reflection of his/her demo- graphic attributes in different scenarios. Based on this, a coupled projection matrix extraction method is proposed for solution, where the cross- OSN behaviors are collectively projected onto the same space for demographic attribute inference. Experimental evaluation is conducted on a self-collected Google+ and Twitter dataset consisting of four types of demographic attributes as gender, age, relationship and occupation. The experimental results demonstrate the effectiveness of cross-OSN based demographic attribute inference.
1:20pm - 1:40pm
Utilizing Locality-Sensitive Hash Learning for Cross-Media Retrieval
1College of Information System and Management, National University of Defense Technology; 2Tsinghua University
Cross-media retrieval is an imperative approach to handle the explosive growth of multimodal data on the web. However, existed approaches to cross-media retrieval are computationally expensive due to the curse of dimensionality. To efficiently retrieve in multimodal data, it is essential to reduce the proportion of irrelevant documents. In this paper, we propose a cross-media retrieval approach (FCMR) based on locality-sensitive hashing (LSH) and neural networks. Multimodal in- formation is projected by LSH algorithm to cluster similar objects into the same hash bucket and dissimilar objects into different ones, using hash functions learned through neural networks. Once given a textual or visual query, it can be efficiently mapped to a hash bucket in which objects stored can be near neighbors of this query. Experimental results show that, in the set of the queries’ near neighbors obtained by the proposed method, the proportions of relevant documents can be much boosted, and it indicats that the retrieval based on near neighbors can be effectively conducted. Further evaluations on two public datasets demonstrate the effectiveness of the proposed retrieval method compared to the baselines.
1:40pm - 2:00pm
CELoF: WiFi Dwell Time Estimation in Free Environment
WiFi wireless access has been the basic living need for smart phone users in the era of mobile multimedia. A large number of WiFi hotspots have also developed into an important infrastructure of multimedia accessing in smart city. Learning the dynamic features of free- environment WiFi connections is of great help to both the customization of WiFi connection service and the strategy of mobile multimedia. While mobility prediction attracts much interest in human behavior research which is more focused on fixed environments such as university, home and office, etc., this paper investigates more challenging public regions like shopping malls. A WiFi dwell time estimation method is proposed from a crowdsourcing view, to tackle the lack of contextual information for a single individual in such free environments. This is achieved by a context-embedded longitudinal factorization (CELoF) method based on multi-way tensor factorization and experiments on real dataset demon- strate the efficacy of the proposed solution.
2:00pm - 2:20pm
Understanding Performance of Edge Prefetching
1Tsinghua University, China, People's Republic of; 2Indiana University, USA
When using online services, the time that users wait for the requested content to be downloaded from online servers to local devices can significantly influence user experience. To reduce user waiting time, the content which are likely to be requested in the future can be pre-downloaded to the local cache on edge proxies (i.e. edge prefetching).
This paper addresses the performance issues of prefetching at edge proxies (e.g. Wi-Fi Access Points (APs), cellular base stations). We introduce an AP-based prefetching framework and study the impact of several factors on the benefit and the cost of this framework based on trace-driven simulation experiments. Useful insights which can be used to guide the design of prediction algorithms and edge prefetching systems are gained from our experimental results. First, increasing prediction window size of the prediction algorithms used by mobile applications can significantly reduce user waiting time. Second, the cache size is important to reducing user waiting time before a certain threshold. Third, the ratio of correct predictions to all actual requests (i.e. recall) can reduce user waiting time linearly while the ratio of correct predictions to all predictions (i.e. precision) will influence the traffic cost, so a trade-off should be made when designing a prediction algorithm.
2:20pm - 2:40pm
User Identification by Observing Interactions with GUIs
1Dublin City University, Ireland; 2Heystaks Technologies Ltd.
Given our increasing reliance on computing devices, the security of such devices becomes ever more important. In this work, we are interested in exploiting user behaviour as a means of reducing the potential for masquerade attacks, which occur when an intruder man- ages to breach the system and act as an authorised user. This could be possible by using stolen passwords or by taking advantage of unlocked, unattended devices. Once the attacker has passed the authentication step, they may have full access to that machine including any private data and software. Continuous identification can be used as an effective way to prevent such attacks, where the identity of the user is checked continuously throughout the session. In addition to security purposes, a reliable dynamic identification system would be of interest for user pro- filing and recommendation. In this paper, we present a method for user identification which relies on modeling the behaviours of a user when interacting with the graphical user interface of a computing device. A publicly-available logging tool has been developed specifically to passively capture human-computer interactions. Two experiments have been conducted to evaluate the model, and the results show the effectiveness and reliability of the method for the dynamic user identification.
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