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Yang, Yang, Luo, Yadan, Chen, Weilun, Shen, Fumin, Shao, Jie, Shen, Heng Tao.  2016.  Zero-Shot Hashing via Transferring Supervised Knowledge. Proceedings of the 2016 ACM on Multimedia Conference. :1286–1295.

Hashing has shown its efficiency and effectiveness in facilitating large-scale multimedia applications. Supervised knowledge (\textbackslashemph\e.g.\, semantic labels or pair-wise relationship) associated to data is capable of significantly improving the quality of hash codes and hash functions. However, confronted with the rapid growth of newly-emerging concepts and multimedia data on the Web, existing supervised hashing approaches may easily suffer from the scarcity and validity of supervised information due to the expensive cost of manual labelling. In this paper, we propose a novel hashing scheme, termed \textbackslashemph\zero-shot hashing\ (ZSH), which compresses images of "unseen" categories to binary codes with hash functions learned from limited training data of "seen" categories. Specifically, we project independent data labels (i.e., 0/1-form label vectors) into semantic embedding space, where semantic relationships among all the labels can be precisely characterized and thus seen supervised knowledge can be transferred to unseen classes. Moreover, in order to cope with the semantic shift problem, we rotate the embedded space to more suitably align the embedded semantics with the low-level visual feature space, thereby alleviating the influence of semantic gap. In the meantime, to exert positive effects on learning high-quality hash functions, we further propose to preserve local structural property and discrete nature in binary codes. Besides, we develop an efficient alternating algorithm to solve the ZSH model. Extensive experiments conducted on various real-life datasets show the superior zero-shot image retrieval performance of ZSH as compared to several state-of-the-art hashing methods.

Ouyang, Deqiang, Shao, Jie, Zhang, Yonghui, Yang, Yang, Shen, Heng Tao.  2018.  Video-Based Person Re-Identification via Self-Paced Learning and Deep Reinforcement Learning Framework. Proceedings of the 26th ACM International Conference on Multimedia. :1562–1570.

Person re-identification is an important task in video surveillance, focusing on finding the same person across different cameras. However, most existing methods of video-based person re-identification still have some limitations (e.g., the lack of effective deep learning framework, the robustness of the model, and the same treatment for all video frames) which make them unable to achieve better recognition performance. In this paper, we propose a novel self-paced learning algorithm for video-based person re-identification, which could gradually learn from simple to complex samples for a mature and stable model. Self-paced learning is employed to enhance video-based person re-identification based on deep neural network, so that deep neural network and self-paced learning are unified into one frame. Then, based on the trained self-paced learning, we propose to employ deep reinforcement learning to discard misleading and confounding frames and find the most representative frames from video pairs. With the advantage of deep reinforcement learning, our method can learn strategies to select the optimal frame groups. Experiments show that the proposed framework outperforms the existing methods on the iLIDS-VID, PRID-2011 and MARS datasets.

Yang, Yang, Ma, Xiaojuan, Fung, Pascale.  2017.  Perceived Emotional Intelligence in Virtual Agents. Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. :2255–2262.

In March 2016, several online news media reported on the inadequate emotional capabilities of interactive virtual assistants. While significant progress has been made in the general intelligence and functionality of virtual agents (VA), the emotional intelligent (EI) VA has yet been thoroughly explored. We examine user's perception of EI of virtual agents through Zara The Supergirl, a virtual agent that conducts question and answering type of conversational testing and counseling online. The results show that overall users perceive an emotion-expressing VA (EEVA) to be more EI than a non-emotion-expressing VA (NEEVA). However, simple affective expression may not be sufficient enough for EEVA to be perceived as fully EI.

Liu, Shuyong, Jiang, Hongrui, Li, Sizhao, Yang, Yang, Shen, Linshan.  2020.  A Feature Compression Technique for Anomaly Detection Using Convolutional Neural Networks. 2020 IEEE 14th International Conference on Anti-counterfeiting, Security, and Identification (ASID). :39–42.
Anomaly detection classification technology based on deep learning is one of the crucial technologies supporting network security. However, as the data increasing, this traditional model cannot guarantee that the false alarm rate is minimized while meeting the high detection rate. Additionally, distribution of imbalanced abnormal samples will lead to an increase in the error rate of the classification results. In this work, since CNN is effective in network intrusion classification, we embed a compressed feature layer in CNN (Convolutional Neural Networks). The purpose is to improve the efficiency of network intrusion detection. After our model was trained for 55 epochs and we set the learning rate of the model to 0.01, the detection rate reaches over 98%.
Xu, Xing, Shen, Fumin, Yang, Yang, Shen, Heng Tao.  2016.  Discriminant Cross-modal Hashing. Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval. :305–308.

Hashing based methods have attracted considerable attention for efficient cross-modal retrieval on large-scale multimedia data. The core problem of cross-modal hashing is how to effectively integrate heterogeneous features from different modalities to learn hash functions using available supervising information, e.g., class labels. Existing hashing based methods generally project heterogeneous features to a common space for hash codes generation, and the supervising information is incrementally used for improving performance. However, these methods may produce ineffective hash codes, due to the failure to explore the discriminative property of supervising information and to effectively bridge the semantic gap between different modalities. To address these challenges, we propose a novel hashing based method in a linear classification framework, in which the proposed method learns modality-specific hash functions for generating unified binary codes, and these binary codes are viewed as representative features for discriminative classification with class labels. An effective optimization algorithm is developed for the proposed method to jointly learn the modality-specific hash function, the unified binary codes and a linear classifier. Extensive experiments on three benchmark datasets highlight the advantage of the proposed method and show that it achieves the state-of-the-art performance.

Yang, Yang, Chang, Xiaolin, Han, Zhen, Li, Lin.  2018.  Delay-Aware Secure Computation Offloading Mechanism in a Fog-Cloud Framework. 2018 IEEE Intl Conf on Parallel Distributed Processing with Applications, Ubiquitous Computing Communications, Big Data Cloud Computing, Social Computing Networking, Sustainable Computing Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom). :346–353.
Fog-Cloud framework is being regarded as a more promising technology to provide performance guarantee for IoT applications, which not only have higher requirements on computation resources, but also are delay and/or security sensitive. In this framework, a delay and security-sensitive computation task is usually divided into several sub-tasks, which could be offloaded to either fog or cloud computing servers, referred to as offloading destinations. Sub-tasks may exchange information during their processing and then have requirement on transmission bandwidth. Different destinations produce different completion delays of a sub-task, affecting the corresponding task delay. The existing offloading approaches either considered only a single type of offloading destinations or ignored delay and/or security constraint. This paper studies a computation offloading problem in the fog-cloud scenario where not only computation and security capabilities of offloading destinations may be different, but also bandwidth and delay of links may be different. We first propose a joint offloading approach by formulating the problem as a form of Mixed Integer Programming Multi-Commodity Flow to maximize the fog-cloud provider's revenue without sacrificing performance and security requirements of users. We also propose a greedy algorithm for the problem. Extensive simulation results under various network scales show that the proposed computation offloading mechanism achieves higher revenue than the conventional single-type computation offloading under delay and security constraints.
Shen, Fumin, Gao, Xin, Liu, Li, Yang, Yang, Shen, Heng Tao.  2017.  Deep Asymmetric Pairwise Hashing. Proceedings of the 2017 ACM on Multimedia Conference. :1522–1530.
Recently, deep neural networks based hashing methods have greatly improved the multimedia retrieval performance by simultaneously learning feature representations and binary hash functions. Inspired by the latest advance in the asymmetric hashing scheme, in this work, we propose a novel Deep Asymmetric Pairwise Hashing approach (DAPH) for supervised hashing. The core idea is that two deep convolutional models are jointly trained such that their output codes for a pair of images can well reveal the similarity indicated by their semantic labels. A pairwise loss is elaborately designed to preserve the pairwise similarities between images as well as incorporating the independence and balance hash code learning criteria. By taking advantage of the flexibility of asymmetric hash functions, we devise an efficient alternating algorithm to optimize the asymmetric deep hash functions and high-quality binary code jointly. Experiments on three image benchmarks show that DAPH achieves the state-of-the-art performance on large-scale image retrieval.
Wu, Fei, Yang, Yang, Zhang, Ouyang, Srinivasan, Kannan, Shroff, Ness B..  2016.  Anonymous-query Based Rate Control for Wireless Multicast: Approaching Optimality with Constant Feedback. Proceedings of the 17th ACM International Symposium on Mobile Ad Hoc Networking and Computing. :191–200.

For a multicast group of n receivers, existing techniques either achieve high throughput at the cost of prohibitively large (e.g., O(n)) feedback overhead, or achieve low feedback overhead but without either optimal or near-optimal throughput guarantees. Simultaneously achieving good throughput guarantees and low feedback overhead has been an open problem and could be the key reason why wireless multicast has not been successfully deployed in practice. In this paper, we develop a novel anonymous-query based rate control, which approaches the optimal throughput with a constant feedback overhead independent of the number of receivers. In addition to our theoretical results, through implementation on a software-defined ratio platform, we show that the anonymous-query based algorithm achieves low-overhead and robustness in practice.