Cross-Document Fact Verification (CDFV) aims to retrieve related evidence from multiple documents to verify the factuality of a given claim, relying on the quality of the retrieved evidence. However, existing CDFV approaches heavily depend on specific heuristics or rule-based strategies, leveraging similarity measures of semantic or surface forms between claims and documents for evidence retrieval. To address the problem above, we propose an Evidential Graph Attention neTwork (EGAT) for CDFV. EGAT utilizes graph attention network to capture relationships between sentences, updating their representations and obtaining more expressive sentence embeddings. To acquire credible evidence, EGAT leverages golden evidence which is manually annotated and capable of verifying the factuality of the claim. Sentences in the graph that are most relevant to the gold evidence are selected as evidence sentences. To enhance the reliability of claim verification, EGAT utilizes a homogeneous network to fuse the information of the claim and evidence, which makes full use of the information provided by evidence and reduces the duplication of work in the process of claim verification. Experimental results confirm the effectiveness of EGAT in retrieving credible evidence and demonstrate improvements in achieving accurate claim verification.

System level tests (SLTs) are important and expensive procedures to ensure high IC quality. In volume production stage with stable high yields, efforts such as random sampling have been used to improve testing efficiency. However random sampling doesn’t fully utilize information gathered before SLT and is not optimal. In this paper we propose both supervised (SVM) and unsupervised (AutoEncoder) machine learning algorithms to predict or estimate SLT failures based on earlier stage Final Test (FT) test data and further use the estimated pseudo probabilities to guide the selecting of dies for system level testing. Experiments on a real product dataset, consisting of 158 wafers, each with 3118 FT testing variables reveal robustness of the models. Through the gains chart of the models, we provide a flexible smart sampling strategy and demonstrate its potential of reducing SLT testing cost by 40% with minor impact on Defective Parts Per Million (DPPM). Our cases also show that such smart sampling approach is very well suited for engaging adaptive test flow optimization achieving balanced goals of improving test efficiency, reducing cost and ensuring high product quality at the same time

System level tests (SLTs) are important and expensive procedures to ensure high quality of IC products. In the volume production stage with stable yield, efforts such as random sampling have been made to improve testing efficiency. However random sampling doesn’t fully utilize information gathered before SLT and is not optimal. In this paper we propose both supervised (SVM) and unsupervised (AutoEncoder ) machine learning algorithms to predict or estimate the SLT failure based on earlier stage Final Test (FT) data and use the estimated pseudo probabilities to guide the selection of some chips for system level test. Experiments on a real product dataset, consisting of 158 wafers from 8 lots, each with 3118 FT testing variables reveal robustness of the models to data shift such as lot variations and missing test items. Through the gains chart of the models, we provide a flexible smart sampling strategy and demonstrate its potential of reducing SLT testing cost by 40% with minor impact on Defective Parts Per Million (DPPM). Our cases also show that such robust machine learning based sampling approach is very well suited for engaging adaptive test flow optimization to achieve balanced goals of improving test efficiency, reducing cost and ensuring high product quality at the same time.

Wafer Bin Maps (WBM) frequently show various spatial failure patterns that provide crucial information for engineers to identify the root cause of failures and their consequent low yield. To shorten the root-cause diagnosis process, it is important to classify different failure patterns with high accuracy, especially when there is mixed type of failure patterns on the same wafer. The main challenges of mixed type classification in WBMs include: 1) Lack of accurately annotated real-world training dataset, 2) Imbalanced/long-tail distributions among classes, 3) Synthesized training data usually cannot reflect the practical application conditions. In this paper, we propose a weakly supervised learning approach and use an ensemble method based on triplet CNN models to classify mixed-type failure patterns in WBMs. We train the models based on the public WM-811K dataset, which is collected from real products but with only single-label annotations. We demonstrate that such models could mitigate the imbalanced class distribution and being able to learn efficiently from a weakly labeled dataset and achieve superior performances on the classification of real wafer maps with long-tail distributed mixed type failures. We also discuss the practical considerations of implementing such models and the advantages of using triplet over binary CNN models.

The new social media have become popular for information spreading, allowing online users to publish latest events and personal opinions. However, massive spam comments seriously decrease users' reading experience. To detect spam comments in Chinese social media, we employ semantic analysis to build the self-extensible dictionary which updates and extends itself with new cyber words automatically. The Semantic analysis brings extra semantic features which helps in text classification. Based on the statistical analysis of microblogging comments, we select four text-based features, which basically represent characteristics of Chinese spam comments. We use spam dictionary and text-based features to construct classifiers for detecting spam comments. Finally, we achieve an average detection accuracy of 93.6%, which is preferable to existing spam comments detection methods. Experimental results demonstrate that our method can effectively detect spam comments in Chinese microblogging field.

The high popularity of Weibo has greatly enriched people's lives, allowing online users to share their feelings through posting comments. However, more and more spam comments are also being posted in users' blogs on this social media. In this paper, in order to effectively detect spam comments in Chinese micro-blogs, we introduce semantic analysis to construct a Self-Extensible Spam Dictionary which automatically expands itself when new words emerge on the micro-blogs frequently. The use of semantic analysis can provide us with additional features which are beneficial to detecting spam comments. A Proportion-Weight Filter (PWF) model is also proposed to detect two kinds of spam comments (AD and vulgar comments), by filtering the spam-weight and the spam-proportion of the Weibo comments based on our Self-Extensible Spam Dictionary criteria. Our experimental results demonstrate that when detecting a combination of both AD and vulgar spam comments, we can achieve an average detection accuracy of 87.9%. Particularly for AD spam comments detection, we can achieve an average accuracy of 96.2%, which is preferable compared to when using machine learning methods. The statistical analysis of the results verifies that our proposed methods can identify the spam comments effectively and to relatively high degrees of accuracy.