Recent studies have suggested that potential short-comings of certain perception modules can be discovered by analyzing the performance of worst scenarios. However, finding the worst perception scenario (WPS) requires datasets with rich semantic annotations of the scenes in visual perception tasks and it is time-consuming to label all the scenario data. To address this, we proposed a method of prediction for WPS, which utilized prior information to predict the model performance under the absence of annotations. Specifically, this paper introduced a scenario matcher based on hybrid re-ranking, which combined the labeled and unlabeled data to generate the pseudo-sample set. In addition, we designed a sample reorganization module to update this sample set through the nearest neighbor retrieval. We also discussed the distribution relationship between labeled and unlabeled data, categorizing it into three cases, and validated the effectiveness of the proposed method on the KITTI and ApolloScape datasets.

Achieving excellent generalization on perceiving real traffic scenarios with diversity is the long-term goal for building robust autonomous driving systems. A recent theoretical study shows that the generalization on the worst-group of test samples is far more difficult than others. Therefore, we propose to discover potential shortness of certain perception module by analyzing its worst-scenario performance. However, with the benchmark datasets growing huge and tremendous, exhaustive searching for the worst perception scenario (WPS) seems to be time consuming and unnecessary. To address this, we present an automatic searching scheme empowered by reinforcement learning. In this case, worst scenario mining is formulated as the discrete search on the Visual Operation Design Domain (ODD), namely scenario representation, by optimizing LSTM-RNN controller with the worst-performance reward. Moreover, a time-efficient K-reciprocal re-ranking technique is utilized to match the predicted scenario parameters with existing test data. The proposed method has been validated by finding the most challenging scenarios for various vehicle detectors on KITTI, BDD100k and our own benchmark set EVB. Furthermore, searching performances w.r.t different Visual ODDs are investigated and it is found that visual representations through generative adversarial network contribute to a better performance.

The key procedure of haze image synthesis with adversarial training lies in the disentanglement of the feature involved only in haze synthesis, i.e.,the style feature, from the feature representing the invariant semantic content, i.e.,the content feature. Previous methods introduced a binary classifier to constrain the domain membership from being distinguished through the learned content feature during the training stage, thereby the style information is separated from the content feature. However, we find that these methods cannot achieve complete content-style disentanglement. The entanglement of the flawed style feature with content information inevitably leads to the inferior rendering of haze images. To address this issue, we propose a self-supervised style regression model with stochastic linear interpolation that can suppress the content information in the style feature. Ablative experiments demonstrate the disentangling completeness and its superiority in density-aware haze image synthesis. Moreover, the synthesized haze data are applied to test the generalization ability of vehicle detectors. Further study on the relation between haze density and detection performance shows that haze has an obvious impact on the generalization ability of vehicle detectors and that the degree of performance degradation is linearly correlated to the haze density, which in turn validates the effectiveness of the proposed method.

Remote Access Trojan (RAT) attacks have become an extensively prevailing and serious threat to enterprise security. A forensic system targeting RAT attacks is needed to record and reconstruct fine-grained semantic behaviors of RATs. However, existing forensic systems suffer from various issues such as intrusive instrumentation, nontrivial recording overhead, and RAT behavior blindness. In this article, we first conduct a large-scale study of a representative set of real-world RAT families active from 1999 to 2016. This is the first study to understand the landscape of RATs in the literature. Based on the study, we then proposeRATScope, an instrumentation-free RAT forensic system targeting Windows platform. Specifically,RATScopeoffers an audit logging module to efficiently record system logs by leveraging Event Tracing for Windows (ETW), and provides a novel program behavior modeling technique to reconstruct semantic behaviors of RATs accurately. We implement a prototype ofRATScopeand evaluate the recording overhead and the behavior identification accuracy. The results show that the audit logging module only incurs 3.7 percent runtime overhead on average. Our system can achieve around 90 percent true positive rate in the cross-family experiment, around 80 percent true positive rate in the two-year spanning temporal experiment, and nearzerofalse positive rate.

Achieving excellent generalization on perceiving real traffic scenarios with diversity is the long-term goal for building robust autonomous driving systems. In this paper, we propose to discover potential shortness of certain perception module by analyzing its worst-scenario performance. However, with the benchmark datasets growing huge and tremendous, exhaustive searching for the worst perception scenario (WPS) seems to be time consuming and unnecessary. To address, we present an automatic searching scheme empowered by reinforcement learning. In this case, worst scenario mining is formulated as a discrete search problem. A single layer recurrent neural network with LSTM neurons is employed to predict WPS according to the searching reward, which is optimized by a vanilla policy gradient method. Moreover, to deal with the imbalanced distribution of real traffic scenarios, a KNN-like retrieval is utilized for searching the closest scenario samples. Effective yet efficient, the proposed method has been validated by finding the most challenging scenarios for various vehicle detectors on KITTI, BDD100k and our own benchmark set EVB. Further experiments reveal that detection networks with structural similarity share the similar WPS.

With the increasing development of Web 2.0, such as social media and online businesses, the need for perception of opinions, attitudes, and emotions grows rapidly.Sentiment analysis, the topic studying such subjective feelings expressed in text, has attracted significant attention from both the research community and industry.Although we have known sentiment analysis as a task of mining opinions expressed in text and analyzing the entailed sentiments and emotions, so far the task is still vaguely defined in the research literature because it involves many overlapping concepts and sub-tasks.Because this is an important area of scientific research, the field needs to clear this vagueness and define various directions and aspects in detail, especially for students, scholars, and developers new to the field.In fact, the field includes numerous natural language processing tasks with different aims (such as sentiment classification, opinion information extraction, opinion summarization, sentiment retrieval, etc.) and these have multiple solution paths.Bing Liu has done a great job in this book in providing a thorough exploration and an anatomy of the sentiment analysis problem and conveyed a wealth of knowledge about different aspects of the field.

Text classification is a foundational task in many NLP applications. Traditional text classifiers often rely on many human-designed features, such as dictionaries, knowledge bases and special tree kernels. In contrast to traditional methods, we introduce a recurrent convolutional neural network for text classification without human-designed features. In our model, we apply a recurrent structure to capture contextual information as far as possible when learning word representations, which may introduce considerably less noise compared to traditional window-based neural networks. We also employ a max-pooling layer that automatically judges which words play key roles in text classification to capture the key components in texts. We conduct experiments on four commonly used datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods on several datasets, particularly on document-level datasets.

Yubo Chen, Liheng Xu, Kang Liu, Daojian Zeng, Jun Zhao. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, Jun Zhao. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.

Mining opinion targets and opinion words from online reviews are important tasks for fine-grained opinion mining, the key component of which involves detecting opinion relations among words. To this end, this paper proposes a novel approach based on the partially-supervised alignment model, which regards identifying opinion relations as an alignment process. Then, a graph-based co-ranking algorithm is exploited to estimate the confidence of each candidate. Finally, candidates with higher confidence are extracted as opinion targets or opinion words. Compared to previous methods based on the nearest-neighbor rules, our model captures opinion relations more precisely, especially for long-span relations. Compared to syntax-based methods, our word alignment model effectively alleviates the negative effects of parsing errors when dealing with informal online texts. In particular, compared to the traditional unsupervised alignment model, the proposed model obtains better precision because of the usage of partial supervision. In addition, when estimating candidate confidence, we penalize higher-degree vertices in our graph-based co-ranking algorithm to decrease the probability of error generation. Our experimental results on three corpora with different sizes and languages show that our approach effectively outperforms state-of-the-art methods.

Extracting opinion targets and opinion words from online reviews are two fun-damental tasks in opinion mining. This paper proposes a novel approach to col-lectively extract them with graph co-ranking. First, compared to previous methods which solely employed opinion relations among words, our method con-structs a heterogeneous graph to model two types of relations, including seman-tic relations and opinion relations. Next, a co-ranking algorithm is proposed to es-timate the confidence of each candidate, and the candidates with higher confidence will be extracted as opinion targets/words. In this way, different relations make coop-erative effects on candidates ’ confidence estimation. Moreover, word preference is captured and incorporated into our co-ranking algorithm. In this way, our co-ranking is personalized and each candi-date’s confidence is only determined by its preferred collocations. It helps to improve the extraction precision. The experimen-tal results on three data sets with differ-ent sizes and languages show that our ap-proach achieves better performance than state-of-the-art methods. 1

Product feature mining is a key subtask in fine-grained opinion mining.Previous works often use syntax constituents in this task.However, syntax-based methods can only use discrete contextual information, which may suffer from data sparsity.This paper proposes a novel product feature mining method which leverages lexical and contextual semantic clues.Lexical semantic clue verifies whether a candidate term is related to the target product, and contextual semantic clue serves as a soft pattern miner to find candidates, which exploits semantics of each word in context so as to alleviate the data sparsity problem.We build a semantic similarity graph to encode lexical semantic clue, and employ a convolutional neural model to capture contextual semantic clue.Then Label Propagation is applied to combine both semantic clues.Experimental results show that our semantics-based method significantly outperforms conventional syntaxbased approaches, which not only mines product features more accurately, but also extracts more infrequent product features.

Question Answering over Linked Data (QALD) aims to evaluate a question an-swering system over structured data, the key objective of which is to translate questions posed using natural language into structured queries. This technique can help common users to directly ac-cess open-structured knowledge on the Web and, accordingly, has attracted much attention. To this end, we propose a novel method using first-order logic. We formulate the knowledge for resolving the ambiguities in the main three steps of QALD (phrase detection, phrase-to-semantic-item mapping and semantic item grouping) as first-order logic clauses in a Markov Logic Network. All clauses can then produce interacted effects in a unified framework and can jointly resolve all am-biguities. Moreover, our method adopts a pattern-learning strategy for semantic item grouping. In this way, our method can cover more text expressions and answer more questions than previous methods us-ing manually designed patterns. The ex-perimental results using open benchmarks demonstrate the effectiveness of the pro-posed method. 1