Pipeline leakages have plagued pipeline transportation for long time. Therefore, accurately extracting the features of leak signal in the presence of noise, and prompt identification of leak states and leak sizes is essential when leakage occurs. A novel leakage detection method based on the improved adaptive filter, whose parameters were optimized by the particle swarm optimization (PSO), was formulated and applied. The PSO-adaptive filter proved to be an effective signal processing method in contrast with variational mode decomposition (VMD). Its efficiency stems from the fact that the adaptive filter employs the noise collected from the detection environment. Therefore, the filter can adjust its parameters according to the changing situation. What is more, the application of PSO is conducive to automatically set suitable parameters for adaptive filter. After signal denoising, principal component analysis (PCA) was used for feature dimension reduction and selecting optimal features. The features after PCA proved to be more helpful in pattern recognition than the features without PCA. Furthermore, the relationship between the recognition results of leakage sizes and the measurement distance of the sensor was studied. Experimental results show that the method used in this paper can identify the leakage states with the accuracy of 100%. The identification result of leakage size reaches an accuracy of 86.75% under the influence of the measurement distance.

Online recommendation services recommend multiple commodities to users. Nowadays, a considerable proportion of users visit e-commerce platforms by mobile devices. Due to the limited screen size of mobile devices, positions of items have a significant influence on clicks: 1) Higher positions lead to more clicks for one commodity. 2) The ‘pseudo-exposure’ issue: Only a few recommended items are shown at first glance and users need to slide the screen to browse other items. Therefore, some recommended items ranked behind are not viewed by users and it is not proper to treat this kind of items as negative samples. While many works model the online recommendation as contextual bandit problems, they rarely take the influence of positions into consideration and thus the estimation of the reward function may be biased. In this paper, we aim at addressing these two issues to improve the performance of online mobile recommendation. Our contributions are four-fold. First, since we concern the reward of a set of recommended items, we model the online recommendation as a contextual combinatorial bandit problem and define the reward of a recommended set. Second, we propose a novel contextual combinatorial bandit method called UBM-LinUCB to address two issues related to positions by adopting the User Browsing Model (UBM), a click model for web search. Third, we provide a formal regret analysis and prove that our algorithm achieves sublinear regret independent of the number of items. Finally, we evaluate our algorithm on two real-world datasets by a novel unbiased estimator. An online experiment is also implemented in Taobao, one of the most popular e-commerce platforms in the world. Results on two CTR metrics show that our algorithm outperforms the other contextual bandit algorithms.

With the rise of online e-commerce platforms, more and more customers prefer to shop online. To sell more products, online platforms introduce various modules to recommend items with different properties such as huge discounts. A web page often consists of different independent modules. The ranking policies of these modules are decided by different teams and optimized individually without cooperation, which might result in competition between modules. Thus, the global policy of the whole page could be sub-optimal. In this paper, we propose a novel multi-agent cooperative reinforcement learning approach with the restriction that different modules cannot communicate. Our contributions are three-fold. Firstly, inspired by a solution concept in game theory named correlated equilibrium, we design a signal network to promote cooperation of all modules by generating signals (vectors) for different modules. Secondly, an entropy-regularized version of the signal network is proposed to coordinate agents’ exploration of the optimal global policy. Furthermore, experiments based on real-world e-commerce data demonstrate that our algorithm obtains superior performance over baselines.

In web search, mutual influences between documents have been studied from the perspective of search result diversification. But the methods in web search is not directly applicable to e-commerce search because of their differences. And little research has been done on the mutual influences between items in e-commerce search. We propose a global optimization framework for mutual influence aware ranking in e-commerce search. Our framework directly optimizes the Gross Merchandise Volume (GMV) for ranking, and decomposes ranking into two tasks. The first task is mutual influence aware purchase probability estimation. We propose a global feature extension method to incorporate mutual influences into the features of an item. We also use Recurrent Neural Network (RNN) to capture influences related to ranking orders in purchase probability estimation. The second task is to find the best ranking order based on the purchase probability estimations. We treat the second task as a sequence generation problem and solved it using the beam search algorithm. We performed online A/B test on a large e-commerce search engine. The results show that our method brings a 5% increase in GMV for the search engine over a strong baseline.

Ranking is a fundamental and widely studied problem in scenarios such as search, advertising, and recommendation. However, joint optimization for multi-scenario ranking, which aims to improve the overall performance of several ranking strategies in different scenarios, is rather untouched. Separately optimizing each individual strategy has two limitations. The first one is lack of collaboration between scenarios meaning that each strategy maximizes its own objective but ignores the goals of other strategies, leading to a sub-optimal overall performance. The second limitation is the inability of modeling the correlation between scenarios meaning that independent optimization in one scenario only uses its own user data but ignores the context in other scenarios. In this paper, we formulate multi-scenario ranking as a fully cooperative, partially observable, multi-agent sequential decision problem. We propose a novel model named Multi-Agent Recurrent Deterministic Policy Gradient (MA-RDPG) which has a communication component for passing messages, several private actors (agents) for making actions for ranking, and a centralized critic for evaluating the overall performance of the co-working actors. Each scenario is treated as an agent (actor). Agents collaborate with each other by sharing a global action-value function (the critic) and passing messages that encodes historical information across scenarios. The model is evaluated with online settings on a large E-commerce platform. Results show that the proposed model exhibits significant improvements against baselines in terms of the overall performance.

The performance of speech recognition systems is consistently poor on non-native speech. The challenge for non-native speech recognition is to maximize the recognition performance with a small amount of available non-native data. We report on acoustic modeling adaptation for the recognition of non-native speech. Using non-native data from German speakers, we investigate how bilingual models, speaker adaptation, acoustic model interpolation and polyphone decision tree specialization methods can help to improve the recognizer performance. Results obtained from the experiments demonstrate the feasibility of these methods.

English, the fast and efficient adaptation to non-native English speech becomes a practical concern. The performance of speech recognition systems is consistently poor on non-native speech. The challenge for non-native speech recognition is to maximize the recognition performance with small amount of non-native data available. In this paper we report on the effectiveness of using polyphone decision tree specialization method for non-native speech adaptation and recognition. Several recognition results are presented by using non-native speech from German speakers. Results obtained from the experiments demonstrate the feasibility of this method.

The increasing interest in multilingual applications like speech-to-speech translation systems is accompanied by the need for speech recognition front-ends in many languages that can also handle multiple input languages at the same time. We describe a universal speech recognition system that fulfills such needs. It is trained by sharing speech and text data across languages and thus reduces the number of parameters and overhead significantly at the cost of only slight accuracy loss. The final recognizer eases the burden of maintaining several monolingual engines, makes dedicated language identification obsolete and allows for code-switching within an utterance. To achieve these goals we developed new methods for constructing multilingual acoustic models and multilingual n-gram language models.

In this paper we report on new developments in the automatic meeting transcription task. Unlike other types of speech (such as those found in Broadcast News and Switchboard), meetings are unique in their richer dynamics of human-to-human interaction. An intuitive "fingernail" plot is proposed to visualize such turntaking behavior. We will also show how recognition of short turns can be improved by building a language model tailored specifically for short turns. Out-Of-Vocabulary (OOV) words become a more salient problem in the meeting transcription task, as they are mostly topic words and proper names, lack of which not only causes Word Error Rate (WER) increase, but also limits further use of recognition hypotheses. We describe a prototype system which uses the Web as a source for vocabulary expansion, and present preliminary OOV retrieval results. 1. INTRODUCTION As speech recognition research progresses from read speech(Wall Street Journal), to prepared speech (a major part of Bro...