Fault management is a key research part in the field of distributed applications management. Fault Management based on active probing is divided into two phases: fault detection and fault diagnosis, considering probe ...
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Recently, Word2Vec tool has attracted a lot of interest for its promising performances in a variety of natural language processing (NLP) tasks. However, a critical issue is that the dense word representations learned ...
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Recently, Word2Vec tool has attracted a lot of interest for its promising performances in a variety of natural language processing (NLP) tasks. However, a critical issue is that the dense word representations learned in Word2Vec are lacking of interpretability. It is natural to ask if one could improve their interpretability while keeping their performances. Inspired by the success of sparse models in enhancing interpretability, we propose to introduce sparse constraint into Word2Vec. Specifically, we take the Continuous Bag of Words (CBOW) model as an example in our study and add the l regularizer into its learning objective. One challenge of optimization lies in that stochastic gradient descent (SGD) cannot directly produce sparse solutions with l regularizer in online training. To solve this problem, we employ the Regularized Dual Averaging (RDA) method, an online optimization algorithm for regularized stochastic learning. In this way, the learning process is very efficient and our model can scale up to very large corpus to derive sparse word representations. The proposed model is evaluated on both expressive power and interpretability. The results show that, compared with the original CBOW model, the proposed model can obtain state-of-the-art results with better interpretability using less than 10% non-zero elements.
This paper shows several security weaknesses of a threshold authenticated encryption scheme. A new threshold authenticated encryption scheme using labor-division signature is proposed without redundancy added to messa...
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This paper shows several security weaknesses of a threshold authenticated encryption scheme. A new threshold authenticated encryption scheme using labor-division signature is proposed without redundancy added to message blocks. On the assumptions of EDDH problems, the proposed scheme is secure against chosen-ciphertext attacks and existentially unforgeable against the chosen- message attacks in the random oracle model.
In order to transmit the secure message, a deterministic secure quantum direct communication protocol which was called ＂Ping-pong＂ protocol was proposed by Bostrrm and Felbinger [Bostrom K, et al. Phys Rev Lett, 200...
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In order to transmit the secure message, a deterministic secure quantum direct communication protocol which was called ＂Ping-pong＂ protocol was proposed by Bostrrm and Felbinger [Bostrom K, et al. Phys Rev Lett, 2002, 89： 187902]. But the protocol was proved very vulnerable, and can be attacked by an eavesdropper. An improved ＂Ping-pong＂ protocol is presented to overcome the problem. The GHZ state particles are used to detect eavesdroppers, and the classical XOR operation which serves as a one-time-pad is used to ensure the security of the protocol. During the security analysis, the method of the entropy theory is introduced, and three detection strategies are compared quantitatively by using the constraint between the information which an eavesdropper can obtain and the interference introduced. If the eavesdropper gets the full information, the detection rate of the original ＂Ping-pong＂ protocol is 50%; the detection rate of the second protocol which used two particles of EPR pair as detection particles is also 50%; and the detection rate of the presented protocol is 75%. In the end, the security of the pro-posed protocol is discussed. The analysis results show that the improved ＂Ping-pong＂ protocol in this paper is more secure than the other two.
This paper addresses face recognition problem in a more challenging scenario where the training and test samples are both subject to the visual variations of poses, expressions and misalignments. We employ dense Scale...
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This paper addresses face recognition problem in a more challenging scenario where the training and test samples are both subject to the visual variations of poses, expressions and misalignments. We employ dense Scale-invariant feature transform(SIFT) feature matching as a generic transformation to roughly align training samples; and then identify input facial images via an improved sparse representation model based on the aligned training samples. Compared with previous methods, the extensive experimental results demonstrate the effectiveness of our method for the task of face recognition on three benchmark datasets.
Dear editor,In the field of image fusion, panchromatic images captured by the Satellite Pour l'Observation de la Terre(SPOT) [1]have high spatial resolutions, whereas their spectral resolutions are relatively low....
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Dear editor,In the field of image fusion, panchromatic images captured by the Satellite Pour l'Observation de la Terre(SPOT) [1]have high spatial resolutions, whereas their spectral resolutions are relatively low. On the other hand, multispectral images have high spectral resolutions; however, their spatial resolutions are low. Because the two types of images captured by satellites cannot meet the demand [2], image fusion is generally necessary in practice.
In this paper, we present an ensemble method for job recommendation to ACM RecSys Challenge 2016. Given a user, the goal of a job recommendation system is to predict those job postings that are likely to be relevant t...
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With the development of mobile Internet, the functionality and portability of mobile device have changed greatly. Many users gradually adapt to using a variety of applications on mobile devices. Mobile applications ar...
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In this paper, we investigate the recommendation task in the most common scenario with implicit feedback (e.g., clicks, purchases). State-of-the-art methods in this direction usually cast the problem as to learn a per...
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The coflow scheduling in data-parallel clusters can improve application-level communication performance. The existing coflow scheduling method without prior knowledge usually uses Multi-Level Feedback Queue (MLFQ) wit...
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