Detecting irony is an important task to mine fine-grained information from social web messages. Therefore, the Semeval-2018 3 aimed detect ironic tweets (subtask A) and their types B). In order address this task, we propose a system based on densely connected LSTM network with multi-task learning strategy. our dense model, each layer will take all outputs previous layers as input. The last output hidden representations of texts, they be used in three classification task. addition,...

Metaphors are figurative languages widely used in daily life and literatures. It's an important task to detect the metaphors evoked by texts. Thus, metaphor shared is aimed extract from plain texts at word level. We propose use a CNN-LSTM model for this task. Our combines CNN LSTM layers utilize both local long-range contextual information identifying metaphorical information. In addition, we compare performance of softmax classifier conditional random field (CRF) sequential labeling also...

Future worldwide 6G research will drive the evolution of emerging intelligent control technologies, such as speed advisory systems (ISA), to a more advanced generation. As special type ISA, consensus-based (CSAS) can be widely used recommend consensus for vehicle platoon, enabling minimizing energy consumption or emissions over planned route. Recently, recommendation services that protect data privacy (i.e., how obtain an optimal in privacy-preserving way) have drawn tremendous attention....

Sentence-level sentiment classification is important to understand users' fine-grained opinions. Existing methods for sentence-level are mainly based on supervised learning. However, it difficult obtain labels of sentences since manual annotation expensive and time-consuming. In this paper, we propose an approach without the need sentence labels. More specifically, a unified framework incorporate two types weak supervision, i.e., document-level word-level labels, learn classifier. addition,...

With the development of social media, a huge number users are attracted by platforms such as Twitter. Emojis widely used network when posting messages. Therefore, it is important to mine relationships between plain texts and emojis. In this paper, we present neural approach predict multiple emojis evoked tweets. Our model contains three modules, i.e., character encoder learn representations words from original characters using convolutional (CNN), sentence sentences combination long...

Sentiment domain adaptation is widely studied to tackle the domain-dependence problem in sentiment analysis field. Existing methods usually train a classifier source and adapt it target using transfer learning techniques. However, when feature distributions of domains are significantly different, performance will heavily decline. In this paper, we propose new approach by adapting knowledge general-purpose lexicons specific domain. Since general words convey consistent sentiments different...

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 cross-lingual entity alignment under This paper proposes an attack model with two novel techniques perturb the KG structure and degrade quality deep alignment. First, density maximization method employed hide attacked entities in dense regions KGs, such derived perturbations unnoticeable. Second, signal amplification...

Emojis are widely used by social media and network users when posting their messages. It is important to study the relationships between messages emojis. Thus, in SemEval-2018 Task 2 an interesting challenging task proposed, i.e., predicting which emojis evoked text-based tweets. We propose a residual CNN-LSTM with attention (RCLA) model for this task. Our combines CNN LSTM layers capture both local long-range contextual information tweet representation. In addition, mechanism select...

Traditional sentiment analysis approaches mainly focus on classifying the polarities or emotion categories of texts. However, they can't exploit intensity information. Therefore, SemEval-2018 Task 1 is aimed to automatically determine emotions tweets mine fine-grained In order address this task, we propose a system based an attention CNN-LSTM model. our model, LSTM used extract long-term contextual information from We apply techniques selecting A CNN layer with different size kernels local...

Cross-lingual entity alignment, which aims to precisely connect the same entities in different monolingual knowledge bases (KBs) together, often suffers challenges from feature inconsistency sequence context unawareness. This paper presents a dual adversarial learning framework for cross-lingual DAEA, with two original contributions. First, order address structural and attribute between graphs (KGs), an kernel embedding technique is proposed extract graph-invariant information unsupervised...

Existing semantic models are capable of identifying the similarity words. However, it’s hard for these to discriminate between a word and another similar word. Thus, aim SemEval-2018 Task 10 is predict whether discriminative attribute two concepts. In this task, we apply multilayer perceptron (MLP)-convolutional neural network (CNN) model identify an discriminative. The CNNs used extract low-level features from inputs. MLP takes both flatten CNN maps inputs labels. evaluation F-score our...

Since it is very difficult to compare linear cutting tool marks quickly and quantitatively using existing image-processing three-dimensional scanning methods, an adaptive matching algorithm for laser detection signals of proposed. Using locally weighted scatterplot smoothing regression, the proposed first performs noise reduction on surface that are detected by a displacement sensor. Trends thick consistent features signal data then identified feature vectors quantised via cosine vector...

Abstract Load forecasting is crucial for the operation and planning of electricity generation, transmission, distribution. In context short‐term load prediction residential users, single‐task learning methods fail to consider relationship among multiple users have limited feature extraction capabilities data. It challenging obtain sufficient information from individual user predictions, resulting in poor performance. To address these issues, we propose a framework multi‐task based on...

Abstract The distributed and privacy‐preserving attributes of fine‐grained smart grid data create obstacles to sharing. As a result, federated learning emerges as an effective strategy for collaborative training in load forecasting. However, poisoning attacks can interfere with the aggregation process, making it challenging ensure accuracy safety global model Therefore, authors propose secure method based on similarity distance (Fed‐SAD) server determines approximate parameters participants...

Autonomous vehicle platooning benefits significantly from the Consensus Speed Advisory System (CSAS), an emerging technology that recommends a consensus speed to reduce energy consumption. However, managing trust ensure system security and identify malicious nodes poses considerable challenge in these autonomous environments. Furthermore, while CSAS optimizes total consumption of platoon, it may inadvertently result increased use for specific vehicles, discouraging their continued...