Due to flexibility, low cost and rapid deployment, wireless sensor networks (WSNs)have been drawing more and more interest from governments, researchers, application developers, and manufacturers in recent years. Nowadays, we are in the age of industry 4.0, in which the traditional industrial control systems will be connected with each other and provide intelligent manufacturing. Therefore, WSNs can play an extremely crucial role to monitor the environment and condition parameters for smart factories. Nevertheless, the introduction of the WSNs reveals the weakness, especially for industrial applications. Through the vulnerability of IWSNs, the latent attackers were likely to invade the information system. Risk evaluation is an overwhelmingly efficient method to reduce the risk of information system in order to an acceptable level. This paper aim to study the security issues about IWSNs as well as put forward a practical solution to evaluate the risk of IWSNs, which can guide us to make risk evaluation process and improve the security of IWSNs through appropriate countermeasures.

Location information of Web pages plays an important role in location-sensitive tasks such as Web search ranking for location-sensitive queries. However, such information is usually ambiguous, incomplete, or even missing, which raises the problem of location prediction for Web pages. Meanwhile, Web pages are massive and often noisy, which pose challenges to the majority of existing algorithms for location prediction. In this paper, we propose a novel and scalable location prediction framework for Web pages based on the query-URL click graph. In particular, we introduce a concept of term location vectors to capture location distributions for all terms and develop an automatic approach to learn the importance of each term location vector for location prediction. Empirical results on a large URL set demonstrate that the proposed framework significantly improves the location prediction accuracy comparing with various representative baselines. We further provide a principled way to incorporate the proposed framework into the search ranking task and experimental results on a commercial search engine show that the proposed method remarkably boosts the ranking performance for location-sensitive queries.

Search engines play a crucial role in our daily lives. Relevance is the core problem of a commercial search engine. It has attracted thousands of researchers from both academia and industry and has been studied for decades. Relevance in a modern search engine has gone far beyond text matching, and now involves tremendous challenges. The semantic gap between queries and URLs is the main barrier for improving base relevance. Clicks help provide hints to improve relevance, but unfortunately for most tail queries, the click information is too sparse, noisy, or missing entirely. For comprehensive relevance, the recency and location sensitivity of results is also critical. In this paper, we give an overview of the solutions for relevance in the Yahoo search engine. We introduce three key techniques for base relevance -- ranking functions, semantic matching features and query rewriting. We also describe solutions for recency sensitive relevance and location sensitive relevance. This work builds upon 20 years of existing efforts on Yahoo search, summarizes the most recent advances and provides a series of practical relevance solutions. The performance reported is based on Yahoo's commercial search engine, where tens of billions of urls are indexed and served by the ranking system.

Click-through logs over query-document pairs provide rich and valuable information for multiple tasks in information retrieval. This paper proposes a vector propagation algorithm on the click graph to learn vector representations for both queries and documents in the same semantic space. The proposed approach incorporates both click and content information, and the produced vector representations can directly improve ranking performance for queries and documents that have been observed in the click log. For new queries and documents that are not in the click log, we propose a two-step framework to generate the vector representation, which significantly improves the coverage of our vectors while maintaining the high quality. Experiments on Web-scale search logs from a major commercial search engine demonstrate the effectiveness and scalability of the proposed method. Evaluation results show that NDCG scores are significantly improved against multiple baselines by using the proposed method both as a ranking model and as a feature in a learning-to-rank framework.

Web search queries provide a surprisingly large amount of information, which can be potentially organized and converted into a knowledgebase. In this paper, we focus on the problem of automatically identifying brand and product entities from a large collection of web queries in online shopping domain. We propose an unsupervised approach based on adaptor grammars that does not require any human annotation efforts nor rely on any external resources. To reduce the noise and normalize the query patterns, we introduce a query standardization step, which groups multiple search patterns and word orderings together into their most frequent ones. We present three different sets of grammar rules used to infer query structures and extract brand and product entities. To give an objective assessment of the performance of our approach, we conduct experiments on a large collection of online shopping queries and intrinsically evaluate the knowledgebase generated by our method qualitatively and quantitatively. In addition, we also evaluate our framework on extrinsic tasks on query tagging and chunking. Our empirical studies show that the knowledgebase discovered by our approach is highly accurate, has good coverage and significantly improves the performance on the external tasks.

Topic models, an unsupervised technique for inferring translation domains improve machine translation quality.However, previous work uses only the source language and completely ignores the target language, which can disambiguate domains.We propose new polylingual tree-based topic models to extract domain knowledge that considers both source and target languages and derive three different inference schemes.We evaluate our model on a Chinese to English translation task and obtain up to 1.2 BLEU improvement over strong baselines.

Spectral methods offer scalable alternatives to Markov chain Monte Carlo and expectation maximization.However, these new methods lack the rich priors associated with probabilistic models.We examine Arora et al.'s anchor words algorithm for topic modeling and develop new, regularized algorithms that not only mathematically resemble Gaussian and Dirichlet priors but also improve the interpretability of topic models.Our new regularization approaches make these efficient algorithms more flexible; we also show that these methods can be combined with informed priors.

We introduce recurrent neural networkbased Minimum Translation Unit (MTU) models which make predictions based on an unbounded history of previous bilingual contexts.Traditional back-off n-gram models suffer under the sparse nature of MTUs which makes estimation of highorder sequence models challenging.We tackle the sparsity problem by modeling MTUs both as bags-of-words and as a sequence of individual source and target words.Our best results improve the output of a phrase-based statistical machine translation system trained on WMT 2012 French-English data by up to 1.5 BLEU, and we outperform the traditional n-gram based MTU approach by up to 0.8 BLEU.

Discovering hierarchical regularities in data is a key problem in interacting with large datasets, modeling cognition, and encoding knowledge. A previous Bayesian solution---Kingman's coalescent---provides a convenient probabilistic model for data represented as a binary tree. Unfortunately, this is inappropriate for data better described by bushier trees. We generalize an existing belief propagation framework of Kingman's coalescent to the beta coalescent, which models a wider range of tree structures. Because of the complex combinatorial search over possible structures, we develop new sampling schemes using sequential Monte Carlo and Dirichlet process mixture models, which render inference efficient and tractable. We present results on both synthetic and real data that show the beta coalescent outperforms Kingman's coalescent on real datasets and is qualitatively better at capturing data in bushy hierarchies.

In this paper, we propose to convert the prosody boundary detection task into a syllable position labeling task. In order to detect both prosodic word and prosodic phrase boundaries, 6 types of syllable positions are defined. For each position, context-dependent position models are trained from manually labeled data. These models are used to label syllable positions in unseen speech. Word and phrase boundaries are then easily derived from syllable position labels. The proposed approach is tested with a large scale single speaker database. The precision and recall for word boundary are 96.1 % and 90.1%, respectively, and for phrase boundary are 83.7 % and 80.5%, respectively. Results of a listening test shows that only 28% of word boundaries and 50 % of phrase of boundaries detected automatically are critical error, implying only about 2.2 % and 10 % errors for word and phrase boundaries, respectively. The results are rather good, especially when it is considered that only acoustic features are used in this work. Index Terms: Boundary detection, context-dependent position model, prosodic word, phrase

In this paper, we study tonal variations by training contextdependent tone models from a large speech corpus. Each model represents a tone-in-context and can output a stylized f0 pattern for it. With these tone models, it becomes tangible to investigate f0-variations with plenty of factors. Six contextual factors are investigated in this work. We find that impact of a factor varies across tones as well as the three states of a tone. Normally, onset pitch of a tone is determined interactively by syllable position and left tone, while, the offset pitch is mainly determined by syllable position. For a neutral tone, its pitch level is mainly depended on the left tone and syllable position affects its offset pitch. Both current vowel and right tone influence the pitch level, yet their impacts are weaker than syllable position and left tone, except for T3, the low tone. Index Terms: tonal variation, context-dependent, tone model 1.