Deep neural networks (DNNs) are vulnerable to backdoor attacks, where an attacker manipulates a small portion of the training data to implant hidden backdoors into the model. The compromised model behaves normally on clean samples but misclassifies backdoored samples into the attacker-specified target class, posing a significant threat to real-world DNN applications. Currently, several empirical defense methods have been proposed to mitigate backdoor attacks, but they are often bypassed by more advanced backdoor techniques. In contrast, certified defenses based on randomized smoothing have shown promise by adding random noise to training and testing samples to counteract backdoor attacks. In this paper, we reveal that existing randomized smoothing defenses implicitly assume that all samples are equidistant from the decision boundary. However, it may not hold in practice, leading to suboptimal certification performance. To address this issue, we propose a certified backdoor defense method with sample-specific smoothing noises, termed Cert-SSBD. Cert-SSBD first employs stochastic gradient ascent to optimize the noise magnitude for each sample, ensuring a sample-specific noise level that is then applied to multiple poisoned training sets to retrain several smoothed models. After that, Cert-SSBD aggregates the predictions of multiple smoothed models to generate the final robust prediction. In particular, in this case, existing certification methods become inapplicable since the optimized noise varies across different samples. To conquer this challenge, we introduce a storage-update-based certification method, which dynamically adjusts each sample’s certification region to improve certification performance. We conduct extensive experiments on multiple benchmark datasets, demonstrating the effectiveness of our proposed method. Our code is available at https://github.com/NcepuQiaoTing/Cert-SSBD.

With the rise of linguistic steganography, the robustness to withstand minor perturbations like textual edits or tokenization shifts remains a critical yet crucial challenge. To address this, we propose a novel robustness-enhancing method, Position-Agnostic Probabilistic Generation, which combines semantic clustering with probabilistic generation control. During decoding, the model is softly guided to prefer or avoid specific token sets through time-aware probability boosting, enabling robust bit embedding without relying on fixed token positions. These token sets are constructed from semantically coherent clusters derived from the language model's vocabulary and expanded to ensure fluency. A dynamic, time-aware boosting strategy is then applied to gradually amplify the likelihood of valid tokens throughout the generation. Experimental results demonstrate that our method consistently outperforms baselines in preserving hidden information under 10 types of perturbations.

Difficulty Controllable Question Generation (DCQG) for reading comprehension learns to generate questions for measuring the reading abilities of examinees, playing a crucial role in educational scenarios. This work studies answer-aware DCQG, a challenging task that requires the generated questions to remain faithful to the assigned answer and match the desired difficulty level at the same time. To this end, we first propose an effective two-stage framework, Asking Questions with Thoughts (AQT), to guide a backbone large language model (LLM) to generate questions that are both faithful and difficulty-aware through conducting in-depth self-thinking. Then, we introduce a novel Posterior Knowledge Distillation (PKD) to efficiently fine-tune AQT by distilling knowledge from posterior inference. Finally, to address the scarcity of DCQG datasets, we use an efficient LLM Pretest-based Difficulty Estimation (LP-DE) to automatically construct DCQG datasets from common QG/QA datasets. Extensive experiments prove that our methods have promising results in terms of both faithfulness and difficulty awareness.

Extensive studies have revealed that deep neural networks (DNNs) are vulnerable to adversarial attacks, especially black-box ones, which can heavily threaten the DNNs deployed in the real world. Many attack techniques have been proposed to explore the vulnerability of DNNs and further help to improve their robustness. Despite the significant progress made recently, existing black-box attack methods still suffer from unsatisfactory performance due to the vast number of queries needed to optimize desired perturbations. Besides, the other critical challenge is that adversarial examples built in a noise-adding manner are abnormal and struggle to successfully attack robust models, whose robustness is enhanced by adversarial training against small perturbations. There is no doubt that these two issues mentioned above will significantly increase the risk of exposure and result in a failure to dig deeply into the vulnerability of DNNs. Hence, it is necessary to evaluate DNNs' fragility sufficiently under query-limited settings in a non-additional way. In this paper, we propose the Spatial Transform Black-box Attack (STBA), a novel framework to craft formidable adversarial examples in the query-limited scenario. Specifically, STBA introduces a flow field to the high-frequency part of clean images to generate adversarial examples and adopts the following two processes to enhance their naturalness and significantly improve the query efficiency: a) we apply an estimated flow field to the high-frequency part of clean images to generate adversarial examples instead of introducing external noise to the benign image, and b) we leverage an efficient gradient estimation method based on a batch of samples to optimize such an ideal flow field under query-limited settings. Compared to existing score-based black-box baselines, extensive experiments indicated that STBA could effectively improve the imperceptibility of the adversarial examples and remarkably boost the attack success rate under query-limited settings.

Knowledge-grounded Dialogue Response Generation (KRG) can facilitate informative and fidelity dialogues using external knowledge. Prior monolingual works can only use the knowledge of the corresponding native language. Thus, due to the prohibitive costs of collecting and constructing external knowledge bases, the limited scale of accessible external knowledge always constrains the ability of KRG, especially in low-resource language scenarios. To this end, we propose a new task, Multi-Source Multilingual Knowledge-Grounded Response Generation (MMKRG), which simultaneously uses multiple knowledge sources of different languages. We notice that simply combining knowledge of different languages is inefficient due to the Cross-Conflict issue and Cross-Repetition issue. Thus, we propose a novel approach MMK-BART, which uses a simple but elegant Estimate-Cluster-Penalize mechanism to overcome the mentioned issues and adopts the multilingual language model mBART as the backbone. Meanwhile, based on the recent multilingual corpus XDailyDialog, we propose an MMKRG dataset MMK-DailyDialog, which has been aligned to the large-scale multilingual commonsense knowledge base ConceptNet and supports four languages (English, Chinese, German, and Italian). Extensive experiments have verified the effectiveness of our dataset and approach in monolingual, cross-lingual, and multilingual scenarios.

Extensive studies have demonstrated that deep neural networks (DNNs) are vulnerable to adversarial examples (AEs), which brings a huge security risk to the application of DNNs, especially for the AI models developed in the real world. To impede the process of fully exploiting the vulnerabilities of existing DNNs and further improving their robustness in the face of such malicious inputs, many attack methods have been proposed to build AEs. Despite the significant progress that has been made recently, existing attack methods still suffer from the unsatisfactory performance of escaping from being detected by naked human eyes due to the formulation of AE heavily relying on a noise-adding manner. Such mentioned challenges will significantly increase the risk of exposure and result in an attack to be failed. Therefore, in this paper, we propose the Salient Spatially Transformed Attack (SSTA), a novel framework to craft imperceptible AEs, which enhance the stealthiness of AEs by estimating a smooth spatial transform metric on a most critical area to generate AEs instead of adding external noise to the whole image. Compared to SOTA baselines, extensive experiments indicated that SSTA could effectively improve the imperceptibility of the AEs while maintaining a 100% attack success rate.

Adversarial examples have the property of transferring across models, which has created a great threat for deep learning models. To reveal the shortcomings in the existing deep learning models, the method of the ensemble has been introduced to the generating of transferable adversarial examples. However, most of the model ensemble attacks directly combine the different models’ output but ignore the large differences in optimization direction of them, which severely limits the transfer attack ability. In this work, we propose a new kind of ensemble attack method called stochastic average ensemble attack. Unlike the existing approach of averaging the outputs of each model as an integrated output, we continuously optimize the ensemble gradient in an internal loop using the model history gradient and the average gradient of different models. In this way, the adversarial examples can be updated in a more appropriate direction and make the crafted adversarial examples more transferable. Experimental results on ImageNet show that our method generates highly transferable adversarial examples and outperforms existing methods.

Future worldwide 6G research will drive the evolution of emerging intelligent control technologies, such as intelligent speed advisory systems (ISA), to a more advanced generation. As a special type of ISA, consensus-based speed advisory systems (CSAS) can be widely used to recommend a consensus speed for a vehicle platoon, enabling minimizing energy consumption or emissions over a planned route. Recently, speed recommendation services that protect data privacy (i.e., how to obtain an optimal speed in a privacy-preserving way) have drawn tremendous attention. However, current approaches could still encounter service trust issues with central servers and the malicious behavior of vehicles. Furthermore, existing research lacks considering road safety constraints (i.e., safe distance between adjacent vehicles and road speed limits) that are essential for the practical deployment of CSAS. To address the above issues, this paper proposes Eco-CSAS, a safe and eco-friendly consensus speed advisory system using blockchain. We formulate an optimization problem subject to the minimum following distance and maximum road speed limit to minimize the energy consumption of the automatic vehicle platoon. In addition, we introduce a consortium blockchain and cryptographic primitives to ensure service trust and data privacy. We implement the system on the Hyperledger platform, and experimental results show that the system can achieve speed recommendations in a trustworthy and privacy-preserving manner while ensuring a secure platoon.

In knowledge-grounded dialogue generation, pre-trained language models (PLMs) can be expected to deepen the fusing of dialogue context and knowledge because of their superior ability of semantic understanding. Unlike adopting the plain text knowledge, it is thorny to leverage the structural commonsense knowledge when using PLMs because most PLMs can only operate plain texts. Thus, linearizing commonsense knowledge facts into plan text is a compulsory trick. However, a dialogue is always aligned to a lot of retrieved fact candidates; as a result, the linearized text is always lengthy and then significantly increases the burden of using PLMs. To address this issue, we propose a novel two-stage framework SAKDP. In the first pre-screening stage, we use a ranking network PriorRanking to estimate the relevance of a retrieved knowledge fact. Thus, facts can be clustered into three sections of different priorities. As priority decreases, the relevance decreases, and the number of included facts increases. In the next dialogue generation stage, we use section-aware strategies to encode the linearized knowledge. The powerful but expensive PLM is only used for a few facts in the higher priority sections, reaching the performance-efficiency balance. Both the automatic and human evaluation demonstrate the superior performance of this work.

Traditional conversational systems can only access the given query during the response generation, leading to meaningless responses. To this end, researchers proposed to enhance dialogue generation by integrating external knowledge. Although such methods have achieved remarkable gains, the use of only single-source knowledge often makes existing knowledge-enhanced methods degenerate into traditional models in real scenarios because of the insufficient knowledge coverage of single-source knowledge. To improve the applicability of knowledge-enhanced methods, we propose two novel frameworks to use heterogeneous knowledge from multiple sources. We first propose an MHKD-Seq2Seq framework, which can use different heterogeneous knowledge by identifying abstract-level knowledge behaviors; meanwhile, a Diffuse-Aggregate scheme is used to process multiple knowledge simultaneously and produce a unified result. The next framework MHKD-ARPLM can leverage the advantages of pretrained language models with Knowledge Linearization techniques. In experiments, we collected dialogues from previously open-released datasets and built a multi-source knowledge-aligned dataset TriKE-Weibo, which involves three knowledge sources: commonsense, texts, and infobox tables. Extensive evaluations demonstrate the performance leadership of our approaches against competitive baseline models.

Dialogues systems endow machines with the ability to converse with humans using natural language. Nonetheless, previous Seq2Seq-based generative dialogue systems often generate safe but meaningless responses, such as ‘I don't know' or ‘I think so'. To this end, researchers proposed to infuse external knowledge into dialogue generation, and such knowledge-enhanced methods have achieved remarkable improvements in the open-domain dialogue systems. External knowledge is an exogenous input, where the estrangement inevitably exists between knowledge and dialogue context. Although previous knowledge-enhanced works can already use commonsense knowledge to generate informative responses, they always use knowledge in a single-channel paradigm, which is hard to accurately handle different data-flows and then tends to generate irrational dialogue responses. Thus, they tend to be confused and generate strange responses when infusing the knowledge into dialogue generation, such as ‘I just ate a basketball’, dramatically degrading the user experience. To address this problem, this paper proposes a novelChannel-Aware Knowledge FusingNetwork (CAKF). Rather than following the traditional single-channel paradigm, CAKF employs three unique channels to handle different data-flows more clearly and rationally: abasechannel serves like a vanilla Seq2Seq decoder; acontextchannel to utilize the contextual information, and aknowledgechannel to infuse commonsense knowledge into the dialogue generation. Above such three channels, aSequential Manageris built to maintain the global sequential decision state, aggregate the local data-flows, and make the final prediction. Experiments on two open-released datasets (a Chinese Weibo and an English Reddit) demonstrated the superior performance of this work against various state-of-the-art approaches.

Despite achieving remarkable performance, previous knowledge-enhanced works usually only use a single-source homogeneous knowledge base of limited knowledge coverage.Thus, they often degenerate into traditional methods because not all dialogues can be linked with knowledge entries.This paper proposes a novel dialogue generation model, MSKE-Dialog, to solve this issue with three unique advantages: ( 1) Rather than only one, MSKE-Dialog can simultaneously leverage multiple heterogeneous knowledge sources (it includes but is not limited to commonsense knowledge facts, text knowledge, infobox knowledge) to improve the knowledge coverage; (2) To avoid the topic conflict among the context and different knowledge sources, we propose a Multi-Reference Selection to better select context/knowledge; (3) We propose a Multi-Reference Generation to generate informative responses by referring to multiple generation references at the same time.Extensive evaluations on a Chinese dataset show the superior performance of this work against various state-of-the-art approaches.To our best knowledge, this work is the first to use the multi-source heterogeneous knowledge in the open-domain knowledge-enhanced dialogue generation.

Recent literatures have shown that knowledge graph (KG) learning models are highly vulnerable to adversarial attacks. However, there is still a paucity of vulnerability analyses of cross-lingual entity alignment under adversarial attacks. This paper proposes an adversarial attack model with two novel attack techniques to perturb the KG structure and degrade the quality of deep cross-lingual entity alignment. First, an entity density maximization method is employed to hide the attacked entities in dense regions in two KGs, such that the derived perturbations are unnoticeable. Second, an attack signal amplification method is developed to reduce the gradient vanishing issues in the process of adversarial attacks for further improving the attack effectiveness.

Nowadays, web data is often used to train a dialogue system. However, noises in web data can disturb the training process, as well as can impact the performance. Consequently, dialogue models tend to be brittle when receiving noisy inputs during the inference. This paper proposes a novel framework, Multi-Path Training (MPT), for training a robust dialogue response generation system. MPT improves the robustness to the noisy training data and the noisy inference queries using three paths. Experimental results show MPT can outperform baselines using the same backbone model, and also prove MPT can improve the robustness to the noise in both the training and inference stage.

A recent study has shown that graph matching models are vulnerable to adversarial manipulation of their input which is intended to cause a mismatching. Nevertheless, there is still a lack of a comprehensive solution for further enhancing the robustness of graph matching against adversarial attacks. In this paper, we identify and study two types of unique topology attacks in graph matching: inter-graph dispersion and intra-graph assembly attacks. We propose an integrated defense model, IDRGM, for resilient graph matching with two novel defense techniques to defend against the above two attacks simultaneously. A detection technique of inscribed simplexes in the hyperspheres consisting of multiple matched nodes is proposed to tackle inter-graph dispersion attacks, in which the distances among the matched nodes in multiple graphs are maximized to form regular simplexes. A node separation method based on phase-type distribution and maximum likelihood estimation is developed to estimate the distribution of perturbed graphs and separate the nodes within the same graphs over a wide space, for defending intra-graph assembly attacks, such that the interference from the similar neighbors of the perturbed nodes is significantly reduced. We evaluate the robustness of our IDRGM model on real datasets against state-of-the-art algorithms.

Due to limited knowledge carried by queries, traditional dialogue systems often face the dilemma of generating boring responses, leading to poor user experience. To alleviate this issue, this paper proposes a novel infobox knowledge-aware dialogue generation approach, HITA-Graph, with three unique features. First, open-domain infobox tables that describe entities with relevant attributes are adopted as the knowledge source. An order-irrelevance Hierarchical Infobox Table Encoder is proposed to represent an infobox table at three levels of granularity. In addition, an Infobox-Dialogue Interaction Graph Network is built to effectively integrate the infobox context and the dialogue context into a unified infobox representation. Second, a Hierarchical Infobox Attribute Attention mechanism is developed to access the encoded infobox knowledge at different levels of granularity. Last but not least, a Dynamic Mode Fusion strategy is designed to allow the Decoder to select a vocabulary word or copy a word from the given infobox/query. We extract infobox tables from Chinese Wikipedia and construct an infobox knowledge base. Extensive evaluation on an open-released Chinese corpus demonstrates the superior performance of our approach against several representative methods.

Recent studies have shown that graph learning models are highly vulnerable to adversarial attacks, and network alignment methods are no exception. How to enhance the robustness of network alignment against adversarial attacks remains an open research problem. In this paper, we propose a robust network alignment solution, RNA, for offering preemptive protection of existing network alignment algorithms, enhanced with the guidance of effective adversarial attacks. First, we analyze how popular iterative gradient-based adversarial attack techniques suffer from gradient vanishing issues and show a fake sense of attack effectiveness. Based on dynamical isometry theory, an attack signal scaling (ASS) method with established upper bound of feasible signal scaling is introduced to alleviate the gradient vanishing issues for effective adversarial attacks while maintaining the decision boundary of network alignment. Second, we develop an adversarial perturbation elimination (APE) model to neutralize adversarial nodes in vulnerable space to adversarial-free nodes in safe area, by integrating Dirac delta approximation (DDA) techniques and the LSTM models. Our proposed APE method is able to provide proactive protection to existing network alignment algorithms against adversarial attacks. The theoretical analysis demonstrates the existence of an optimal distribution for the APE model to reach a lower bound. Last but not least, extensive evaluation on real datasets presents that RNA is able to offer the preemptive protection to trained network alignment methods against three popular adversarial attack models.

Generative dialogue systems tend to produce generic responses, which often leads to boring conversations.For alleviating this issue, Recent studies proposed to retrieve and introduce knowledge facts from knowledge graphs.While this paradigm works to a certain extent, it usually retrieves knowledge facts only based on the entity word itself, without considering the specific dialogue context.Thus, the introduction of the context-irrelevant knowledge facts can impact the quality of generations.To this end, this paper proposes a novel commonsense knowledge-aware dialogue generation model, ConKADI.We design a Felicitous Fact mechanism to help the model focus on the knowledge facts that are highly relevant to the context; furthermore, two techniques, Context-Knowledge Fusion and Flexible Mode Fusion are proposed to facilitate the integration of the knowledge in the ConKADI.We collect and build a large-scale Chinese dataset aligned with the commonsense knowledge for dialogue generation.Extensive evaluations over both an open-released English dataset and our Chinese dataset demonstrate that our approach ConKADI outperforms the state-of-the-art approach CCM, in most experiments.

Emotion-cause pair extraction, which aims at extracting both the emotion and its corresponding cause in text, is a significant and challenging task in emotion analysis. Previous work formulated the task in a two-step framework, i.e., emotion and cause extraction, and emotion-cause relation classification. However, different tasks may correlate with each other and the two-step framework does not fully exploit the interactions between tasks. In this paper, we propose a multi-task neural network to perform emotion-cause pair extraction in a unified model. The task of relation classification is learned together with emotion and cause extraction. To this end, we develop a method to obtain training samples for relation classification without the dependence on the result of emotion and cause extraction. To fully exploit the interactions between different tasks, our model shares useful features across tasks. Moreover, we propose a method to incorporate position-aware emotion information in cause extraction to further improve the performance. Experimental results show that our model outperforms the state-of-the-art model on emotion-cause pair extraction.

Insufficient semantic understanding of dialogue always leads to the appearance of generic responses, in generative dialogue systems. Recently, high-quality knowledge bases have been introduced to enhance dialogue understanding, as well as to reduce the prevalence of boring responses. Although such knowledge-aware approaches have shown tremendous potential, they always utilize the knowledge in a black-box fashion. As a result, the generation process is somewhat uncontrollable, and it is also not interpretable. In this paper, we introduce a topic fact-based commonsense knowledge-aware approach, TopicKA. Different from previous works, TopicKA generates responses conditioned not only on the query message but also on a topic fact with an explicit semantic meaning, which also controls the direction of generation. Topic facts are recommended by a recommendation network trained under the Teacher-Student framework. To integrate the recommendation network and the generation network, this paper designs four schemes, which include two non-sampling schemes and two sampling methods. We collected and constructed a large-scale Chinese commonsense knowledge graph. Experimental results on an open Chinese benchmark dataset indicate that our model outperforms baselines in terms of both the objective and the subjective metrics.

Real-world graphs are usually very sparse in terms of inadequate edges and labels as well as have poor quality due to a large amount of noisy data. In this paper, we propose a classification-based local vertex ranking architecture through dual generative adversarial networks in the semi-supervised setting, DQGAN, for analyzing sparse noisy graphs with rarely labeled data. First, we develop a quadruple generative adversarial ClassNet model to address the noisy data and data sparsity issues as well as to classify each vertex into K classes by automatically creating imaginary/real-looking supplementary labeled vertices with the quite different/similar distributions as real vertices, without the high cost of multi-step graph propagation, heterogeneous graph mining, and iterative weight learning. In addition, the vertex label vicinity is incorporated into the classification model to capture the pairwise vertex closeness based on the labeling and align the vertex label vicinity with the well-known vertex homophily for preserving the original structural semantics in the classification space. Second, we present a quintuple generative adversarial RankNet framework to locally rank each vertex on each of K classes by designing the game of multiple competitors utilizing the mix of real and noisy data to fight against each other, for improving the robustness of local vertex ranking to noisy data with few help from human efforts. The cycle ranking consistency strategy is designed to make the ranking quality verifiable through the bidirectional information-lossless translations between the original features and the ranking features. We propose to utilize the relaxed local PageRank property to produce high-quality local vertex ranking results in the context of information networks. Third but last, extensive evaluation on real graph datasets demonstrates that DQGAN outperforms existing representative methods in terms of both classification and ranking in the semi-supervised setting.

Recent years have witnessed a surge of interest on response generation for neural conversation systems. Most existing models are implemented by following the Encoder-Decoder framework and operate sentences of conversations at word-level. The word-level model is suffering from the Unknown Words Issue and the Preference Issue, which seriously impact the quality of generated responses, for example, generated responses may become irrelevant or too general (i.e. safe responses). To address these issues, this paper proposes a hybrid-level Encoder-Decoder model (HL-EncDec), which not only utilizes the word-level features but also character-level features. We conduct several experiments to evaluate HL-EncDec on a Chinese corpus, experimental results show our model significantly outperforms other non-word-level models in automatic metrics and human annotations and is able to generate more informative responses. We also conduct experiments with a small-scale English dataset to show the generalization ability.

Recent years have witnessed a surge of interest in building conversation systems such as smart agents or chatbots. The most existing generation-based neural responding conversation systems are implemented by RNN Encoder-Decoder framework relying on word-level modeling with explicit segmentation. A word-level model typically maintains a fixed vocabulary, which correspondingly encounters the unknown words and segmentation issues. In this paper, we proposed a fully character-level Encoder-Decoder model DLCEncDec without explicit segmentation for neural responding conversation. DLCEncDec utilizes both of fine-grained character embedding features and coarse-grained n-gram features. Coarse-grained n-gram features are captured by constructing a convolutional layer and a four-layer highway network on the top of the character embeddings. The appearance of out-of-vocabulary words (i.e. unknown words) can be addressed due to the fully character-level operating. We evaluate the DLCEncDec on a Chinese corpus consisting of 4.44 million message-response pairs from Sina Weibo. Experimental results show that our fully character-level model DLCEncDec significantly outperforms baseline models in terms of BLEU and ROUGE.

Traditional network representation learning techniques aim to learn latent low-dimensional representation of vertices in graphs. This paper presents a novel edge representation learning framework, GANDLERL, that combines generative adversarial network based multi-label classification with density-adaptive local edge representation learning for producing high-quality low-dimensional edge representations. First, we design a generative adversarial network based multi-label edge classification model to classify rarely labeled edges in graphs with a large amount of noise data into K classes. A four-player zero-sum game model, with the mixed training of true and real-looking fake edges as well as a contrastive loss containing a similar-loss and a dissimilar-loss, is proposed to improve the classification quality of unlabeled edges. Second, a local autoencoder edge representation learning method is developed to design K local representation learning models, each with individual parameters and structure to perform local representation learning on each of K classification-based subgraphs with unique local characteristics and jointly optimize the loss functions within and across classes. Third but last, we propose a density-adaptive edge representation learning method with the optimization at both edge and subgraph levels to address the representation learning of graph data with highly imbalanced vertex degree and edge distribution.

Sentence-level sentiment classification is important to understand users' fine-grained opinions. Existing methods for sentence-level sentiment classification are mainly based on supervised learning. However, it is difficult to obtain sentiment labels of sentences since manual annotation is expensive and time-consuming. In this paper, we propose an approach for sentence-level sentiment classification without the need of sentence labels. More specifically, we propose a unified framework to incorporate two types of weak supervision, i.e., document-level and word-level sentiment labels, to learn the sentence-level sentiment classifier. In addition, the contextual information of sentences and words extracted from unlabeled sentences is incorporated into our approach to enhance the learning of sentiment classifier. Experiments on benchmark datasets show that our approach can effectively improve the performance of sentence-level sentiment classification.

Since it is very difficult to compare linear cutting tool marks quickly and quantitatively using existing image‐processing and three‐dimensional scanning methods, an adaptive matching algorithm for laser detection signals of linear cutting marks is proposed. Using locally weighted scatterplot smoothing regression, the proposed algorithm first performs noise reduction on the surface signals of linear cutting tool marks that are detected by a laser displacement sensor. Trends of the thick and consistent features of the signal data are then identified and the feature vectors are quantised via cosine vector curve fitting to individually calculate the spatial distances of the samples. Finally, the most similar samples are matched via batch similarity comparison using a dynamic programming (DP) algorithm. The accuracy and validity of the proposed algorithm are verified by similarity comparison tests of actual cutting marks from a variety of samples.

Sentiment domain adaptation is widely studied to tackle the domain-dependence problem in sentiment analysis field. Existing domain adaptation methods usually train a sentiment classifier in a source domain and adapt it to the target domain using transfer learning techniques. However, when the sentiment feature distributions of the source and target domains are significantly different, the adaptation performance will heavily decline. In this paper, we propose a new sentiment domain adaptation approach by adapting the sentiment knowledge in general-purpose sentiment lexicons to a specific domain. Since the general sentiment words of general-purpose sentiment lexicons usually convey consistent sentiments in different domains, they have better generalization performance than the sentiment classifier trained in a source domain. In addition, we propose to extract various kinds of contextual sentiment knowledge from massive unlabeled samples in target domain and formulate them as sentiment relations among sentiment expressions. It can propagate the sentiment information in general sentiment words to massive domain-specific sentiment expressions. Besides, we propose a unified framework to incorporate these different kinds of sentiment knowledge and learn an accurate domain-specific sentiment classifier for target domain. Moreover, we propose an efficient optimization algorithm to solve the model of our approach. Extensive experiments on benchmark datasets validate the effectiveness and efficiency of our approach.