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Natural user interaction in virtual environment is a prominent factor in any mixed reality applications. In this paper, we revisit the assessment of natural user interaction via a case study of a virtual aquarium. Viewers with the wearable headsets are able to interact with virtual objects via head orientation, gaze, gesture, and visual markers. The virtual environment is operated on both Google Cardboard and HoloLens, the two popular wireless head-mounted displays. Evaluation results reveal the preferences of users over different natural user interaction methods.

Document vectorization with an appropriate encoding scheme is an essential component in various document processing tasks, including text document classification, retrieval, or generation. Training a dedicated document in a specific domain may require large enough data and sufficient resource. This motivates us to propose a novel document representation scheme with two main components. First, we train TD2V, a generic pre-trained document embedding for English documents from more than one million tweets in Twitter. Second, we propose a domain adaptation process with adversarial training to adapt TD2V to different domains. To classify a document, we use the rank list of its similar documents using query expansion techniques, either Average Query Expansion or Discriminative Query Expansion. Experiments on datasets from different online sources show that by using TD2V only, our method can classify documents with better accuracy than existing methods. By applying adversarial adaptation process, we can further boost and achieve the accuracy on BBC, BBCSport, Amazon4, 20NewsGroup datasets. We also evaluate our method on a specific domain of sensitivity classification and achieve the accuracy of higher than $$95\%$$ even with a short text fragment having 1024 characters on 5 datasets: Snowden, Mormon, Dyncorp, TM, and Enron.

Most popular systems for object instance search are based on the bag-of-visual-word model. The inherent weaknesses of this standard model such as quantization error, unstructured representation, burstiness phenomenon are to some extent solved. However, it has a serious problem of searching small objects on a database with cluttered background. In many situations, even the irrelevant objects which share the same texture or shape with a query object get higher score than relevant ones. To overcome this problem, we propose a novel fusion method to significantly boost the accuracy of instance search systems. Firstly, we use the state-of-the-art object detector with denser feature for finding object bounding box and similarity score. Secondly, to exploit the spatial relationship of each visual word with an object proposal, a detected area that might contain a query object, we define three categories of visual word pairs, i.e., discriminative, weak relevant, and context inferred ones. Finally, we propose a new re-ranking scheme with three weighting functions corresponding to the three categories of visual word pairs to compute the final similarity score between a query topic and a video shot. To illustrate the efficiency of the proposed method, we conduct experiments on datasets which have a wide variety of types of query objects. Experimental results on TRECVID Instance Search datasets (INS2013 and INS2014) show the superiority of our proposed method over the state-of-the-art approaches.

With non-stop development of e-commerce and different internet-based applications’ demands, service providers include many physical servers scattering all over the world. If some users would like to use various services, they have to repeatedly register. Therefore, they must remember all these information such as, user-names or passwords. To solve this problem, many authentication schemes in multi-server environment are proposed. Furthermore, to prevent the adversary from keeping track of another user when he or she logins, many schemes apply dynamic identity, but they have some limitations with popular kinds of attacks such as, replay attack, impersonation attack, or man-in-the-middle attack... In 2014, Yeh (Department of Information Management, National Dong Hwa University) proposed multi-server scheme using Rivest–Shamir–Adleman (RSA). However, we discover this scheme cannot achieve two basic properties, mutual authentication and session-key agreement. In this paper, we concretely demonstrate that discovery and propose a different provable version using elliptic curve cryptosystem in multi-server environment which overcomes the limitation of Yeh’s scheme and satisfies security and efficiency.

With non-stop growth in network environments, communication security is necessary. A strong protocol guarantees that users and service providers are secure against many kinds of attacks, such as impersonation and replay attack. Sood et al. proposed an authentication scheme based on dynamic identity to prevent transactions from being intercepted by malicious users. Although they claimed that their scheme has advantages over previous schemes with the same approach, we prove that their scheme is vulnerable to impersonation attack and stolen verification attack, and can be affected by clock synchronization. Therefore we propose a novel authentication scheme to enhance security and overcome limitations existing in Sood's scheme. Our security analysis shows that our proposed method can efficiently resist known types of attacks. Experimental results also show that the method can be implemented and processed in real-time thus applicable for not only regular computers but also mobile devices.

We propose and analyze in details the revised model of XPROB, an infinite family of pool-based anonymous communication systems that can be used in various applications including high performance computing environments. XPROB overcomes the limitations of APROB Channel that only resists a global delaying adversary (GDA). Each instance of XPROB uses a pool mix as its core component to provide resistance against a global active adversary (GAA), a stronger yet more practical opponent than a GDA. For XPROB, a GAA can drop messages from users but cannot break the anonymity of the senders of messages. Analysis and experimental evaluations show that each instance of XPROB provides greater anonymity than APROB Channel for the same traffic load and user behaviors (rate and number of messages sent). In XPROB, any message can be delivered with high probability within a few rounds after its arrival into the system; thus, an opponent cannot be certain when a message will be delivered. Furthermore, users can choose their own preference balance between anonymity and delay. Through the evaluation, we prove that XPROB can provide anonymity for users in high-performance computing environments.