Weakly Supervised temporal Article Grounding (WSAG) is an important task in the field of video understanding and retrieval, which aims to ground sentences from the article with the corresponding video clips (segments), using only video-text pairwise annotations during training. Existing methods typically rely on a proposal-based approach that utilizes contrastive learning to score candidate video clips. However, these approaches often face two main drawbacks: (1) They mainly focus on global video-level alignment with the article while neglecting the multi-level relationships between video clips and text hierarchy; (2) They overlook intra-modal feature learning, failing to adequately distinguish salient patterns. To address these issues, we propose a novel multi-granularity game-theoretic method, namely Hierarchical Cooperative Network (HCN), which models video clips and article units (i.e., at word, sentence, and paragraph layers) as players in a game. Specifically, our proposed method introduces two mechanisms: (1) Intra-modal feature cooperation: Within each modality, we encourage cooperation among the features to obtain a salient feature set as the effective representations, enabling a sharper distinction between semantically overlapping video clips and enhancing discriminative textual cues; (2) Layered cross-modal cooperation: For different semantic layers of the article, we employ the Shapley interaction index to quantify and promote synergistic effects to further enhance cross-modal matching. By decomposing multi-modal interactions into intra-modal and cross-modal cooperations with multi-layer semantics, HCN achieves fine-grained alignment and strengthens salient feature representations for the WSAG task. Experimental results demonstrate that HCN achieves the state-of-the-art performances on representative WSAG benchmarks, significantly outperforming existing weakly supervised methods and competitive video large language models.

Graph representation learning is a fundamental research theme and can be generalized to benefit multiple downstream tasks from the node and link levels to the higher graph level. In practice, it is desirable to develop task-agnostic graph representation learning methods that are typically trained in an unsupervised manner. However, existing unsupervised graph models, represented by the variational graph auto-encoders (VGAEs), can only address node- and link-level tasks while manifesting poor generalizability on the more difficult graph-level tasks because they can only keep low-orderisomorphic consistencywithin the subgraphs of one-hop neighborhoods. To overcome the limitations of existing methods, in this paper, we propose the Isomorphic-Consistent VGAE (IsoC-VGAE) for multi-level task-agnostic graph representation learning. We first devise an unsupervised decoding scheme to provide a theoretical guarantee of keeping the high-order isomorphic consistency within the VGAE framework. We then propose the Inverse Graph Neural Network (Inv-GNN) decoder as its intuitive realization, which trains the model via reconstructing the node embeddings and neighborhood distributions learned by the GNN encoder. Extensive experiments on multi-level graph learning tasks verify that our model achieves superior or comparable performance compared to both the state-of-the-art unsupervised methods and representative supervised methods with distinct advantages on the graph-level tasks.

Knowledge graph embedding, which aims to learn representations of entities and relations in large scale knowledge graphs, plays a crucial part in various downstream applications. The performance of knowledge graph embedding models mainly depends on the ability of modeling relation patterns, such as symmetry/antisymmetry, inversion and composition (commutative composition and non-commutative composition). Most existing methods fail in modeling the non-commutative composition patterns. Several methods support this kind of pattern by modeling in quaternion space or dihedral group. However, extending to such sophisticated spaces leads to a substantial increase in the amount of parameters, which greatly reduces the parameter efficiency. In this paper, we propose a new knowledge graph embedding method called dual complex number knowledge graph embeddings (DCNE), which maps entities to the dual complex number space, and represents relations as rotations in 2D space via dual complex number multiplication. The non-commutativity of the dual complex number multiplication empowers DCNE to model the non-commutative composition patterns. In the meantime, modeling relations as rotations in 2D space can effectively improve the parameter efficiency. Extensive experiments on multiple benchmark knowledge graphs empirically show that DCNE achieves significant performance in link prediction and path query answering.

Article comment generation is a novel and challenging task in natural language generation, which has attracted widespread attention from researchers in recent years. High-quality article comments such as relevant, diverse, and informative ones can greatly promote user interactions and enhance the user experience. However, current research works generally overlook the relevance between comments and the source article, which may generate mediocre and dull comments. To address this problem, a variational multi-layer fusion model (VMFM) based on variational auto-encoder (VAE) is proposed in this paper. The posterior distribution of the proposed VMFM is employed to supervise the prior network in selecting context-related latent variables from the source article, which are further integrated into the decoder to increase the relevance between generated comments and the source article. Due to the sequential nature of text generation, the influence of those latent variables on the decoder gradually diminishes during auto-regressive decoding. To mitigate this issue, we propose a multi-layer fusion method, which fuses a series of context-related latent variables extracted from the source article into every decoder layer. Experiments on four datasets show that our model significantly outperforms strong baselines in relevance, diversity, informativeness and fluency of generated comments based on automatic and human evaluations.

Security-related events are continuously emerging around the world, such as social unrest, armed conflicts and natural disasters. Although event knowledge graphs provide a concise and intuitive way to organize event information, they often suffer from the incompleteness problem. To solve this problem, the knowledge graph reasoning technique is usually adopted to infer new facts based on existing relations of entities. However, existing reasoning methods often concentrate on the entity relations, without considering the attributes of nodes for event knowledge graphs, such as event descriptions and event time, which contain rich information of events and are vital for event relation reasoning. Existing methods are also deficient in interpretability, which significantly benefits security-related decision-making scenarios. In this paper, we propose a Neuro-Logic Learning (NLL) method for relation reasoning over event knowledge graph. Our method first adopts different event attributes to learn the semantic representations of events, and ranks candidate event relation triples according to their similarity scores. The proposed method then develops the neural logic rule leaner to obtain interpretable reasoning rules. We construct a new large-scale event relation dataset EventKG-R based on EventKG to evaluate the effectiveness of our proposed method. Experimental results show that our proposed method achieves superior performances compared to the state-of-the-art baselines.

Question answering systems have emerged as an important research area in the field of natural language processing, enabling the provision of accurate answers to user queries in a more efficient and user-friendly manner. These systems have much practical significance especially in security-related applications, where intelligence analysts can easily access pertinent information from diverse sources to accelerate the decision-making process. In the context of security-related scenarios, users tend to interact with a question answering system with specific domain-oriented purposes. However, most existing question answering systems focus on open-domain situations with abundant labeled data as well as structured data, and domain-specific question answering methods are in urgent need. Domain-specific question answering faces the challenge of the low-resource issue caused by data scarcity. To address this challenge, in this paper, we propose a weakly supervised self-training method for domain-specific question answering based on the Retriever-Reader framework. For the retriever module, during the self-training process, we develop two strategies for generating pseudo-labels to augment the labeled dataset, including high confidence sampling and random negative sampling. For the reader module, we adopt the pre-trained language model and fine-tune the generative reader using limited labeled datasets. To evaluate our proposed method, we construct the first Chinese financial question answering dataset of textual document. Experimental results demonstrate that our proposed method can significantly improve the performances of the baseline method through the self-training process.

News comments provide a convenient way for people to express opinions and exchange ideas. Positive comments en-courage a harmonious discussion atmosphere within news media communities. In contrast, offensive or insulting comments may result in cyberbullying and personal psychological trauma, which have particular practical impacts in security-related domains. The automatic generation of news comments with controllable attributes (e.g. sentiment) to assist users and news platform administrators is greatly needed. However, existing research for news comment generation has not addressed the controllable issue yet. On the other hand, existing methods for controllable text generation focus on token-level constraints, which are not applicable to controlling the sentence level attributes for news comment generation. To address this challenging issue, in this paper, we propose an attribute-level contrastive learning method for controllable news comment generation. To apply attribute level constraints on the generated text, our method considers the attributes of the generated comments and the pre-defined attributes as different views of the same attribute, and maximizes their similarity during the training process. We conduct experiments on two publicly available news comment datasets, and the experimental results show that our model achieves competitive performance in news comment generation and attribute controllability.

The Impedance-source inverters have been proposed for integrating voltage-boost, buck and inversion into a single stage with shared functionalities and hence lesser active switches. Recently, some of them even have coupled inductors included for raising gain, while retaining high dc-link utilization without extra components. However, leakage inductances of the coupled inductors have caused noticeable problems, like voltage spikes and resonances, during switching transients. These problems are not easy to analyze theoretically with traditional inverter models, because of tight topological integration within each impedance-source inverter. It is therefore the theme hereon to propose a novel model for analyzing transient and power losses of a coupled-inductor impedance-source inverter at each of its switching transitions. Readings from experiments have promptly verified the developed theoretical model for a coupled-inductor impedance-source inverter.

The unprecedented growth of information on the Internet has brought about the problem of information overload. To alleviate this problem, news recommendation aims to select news articles for users according to their personal interests. In security applications such as intelligence collection and public opinion monitoring, it is of great importance to obtain valuable information quickly from massive news resources. Different from other application settings, users in security-related scenarios tend to browse news with a domain-oriented purpose. In contrast to the existing news recommendation methods which focus on general-purpose solutions, news recommendation in security applications needs domain-oriented solutions to incorporate users’ interests in a specific domain. To this end, in this paper, we propose the problem of domain-oriented news recommendation and develop a specific news recommendation model for security applications. Specifically, our proposed Domain-oriented News Recommendation (DNR) model extracts both general and specific preferences of the user, and performs matching between the user and the candidate news from the above two aspects to combine into the final result. We construct three security-related datasets using a large-scale real-world dataset and validate the effectiveness of our method.

Identifying hidden nodes in social networks is a critical issue in security-related applications. In contrast to the conventional node classification on graphs with all nodes being observable, it is more challenging to classify the hidden nodes that are unobservable during the training process, also known as the “inductive learning” in previous research. Existing approaches for inductive node classification mainly adopt graph neural network models to learn node representations. Although these methods are advantageous to modeling the topology of graph-structured data, they rely heavily on node features which may vary significantly in different specific application scenarios. In addition, the inherently changeable graph structure induced by hidden nodes may cause the over-fitting problem. To address the above issues and boost the performances of hidden node classification, we propose a deep generative model based on variational auto-encoders. Specifically, we design a novel graph neural network to aggregate the multi-hop neighbor information of each node. Meanwhile, to better utilize the graph structure information as a supplement to node features, we consider the heterogeneous node influences and introduce a gated attention mechanism using node degrees. Moreover, our proposed model can be trained by minibatches and thus is applicable to large social networks. We conduct experiments on four real-world datasets, and verify the effectiveness of our method for hidden graph node classification.

Social emotion refers to the emotion evoked to the reader by a textual document. Compared to classical sentiment analysis conducted from the author's perspective, emotion analysis from the reader's perspective (i.e. social emotion mining) is an important task in web-based social media analytics, and particularly meaningful for security related applications. Mining the causes of social emotions, i.e. Social Emotion Cause Extraction (SECE), is a new challenging task in social emotion mining, which can help better explain the elicited social emotions in text. In this paper, we propose the SECE task for the first time, and construct a new SECE dataset to support our study. We develop the first computational method for this new task, and conduct experimental studies to evaluate the effectiveness of our proposed method based on the dataset we construct.

The popularity of online contents in social media frequently experiences ebb and flow, and thus its evolution often involves different stages, such as burst and valley. Exploring the patterns of popularity evolution, especially how burst forms and decays, and even further, predicting the trends of popularity evolution is both an important research topic and beneficial to support decision making for many applications, such as emergency management, business intelligence, and public security. Previous work on popularity prediction has focused on predicting the popularity volume of online contents, and at most, popularity burst and ignored the exploration of popularity evolution and the prediction of its stages. To fill this gap, in this paper, we propose our method for the popularity stage prediction problem both at the microscopic level and macroscopic level. At the microscopic level, we first extract multiple dynamic factors and infer future evolution stage by considering the contributions of different dynamic factors. At the macroscopic level, we extract the overall evolution patterns of popularity stages and adopt a pattern matching-based method to predict future popularity stages. We evaluate the proposed approach using tweets in SinaWeibo, the most popular Twitter-like social media platform in China. The experimental results show the effectiveness of our proposed approach in predicting popularity evolution stages.

Cognitive dissonance is a ubiquitous phenomenon which can be applied in various fields potentially. In this paper, we study cognitive dissonance through empirical analysis on social media platforms. Our study focuses on a recent “reversal event” - a topic or event experiencing a reversed development trend because of the new facts. Through statistical analysis and semantic analysis based methods, we found that (1) after the event is revised, the performance of the original followers were abnormal, which is consistent with the existence of cognitive dissonance; (2) the followers' attitude afterwards usually tended to maintain their previous behaviors. This research provides a primary building block towards the mental inference based behavior prediction for social media users, which is of great value for security related research issues.

Social media, such as Facebook and Twitter, has become one of the most important channels for information dissemination. However, these social media platforms are often misused to spread rumors, which has brought about severe social problems, and consequently, there are urgent needs for automatic rumor detection techniques. Existing work on rumor detection concentrates more on the utilization of textual features, but diffusion structure itself can provide critical propagating information in identifying rumors. Previous works which have considered structural information, only utilize limited propagation structures. Moreover, few related research has considered the dynamic evolution of diffusion structures. To address these issues, in this paper, we propose a Neural Model using Dynamic Propagation Structures (NM-DPS) for rumor detection in social media. Firstly, we propose a partition approach to model the dynamic evolution of propagation structure and then use temporal attention based neural model to learn a representation for the dynamic structure. Finally, we fuse the structure representation and content features into a unified framework for effective rumor detection. Experimental results on two real-world social media datasets demonstrate the salience of dynamic propagation structure information and the effectiveness of our proposed method in capturing the dynamic structure.

Social media platforms make the spread of social event information quicker and more convenient. Some of these social events may become hot topics, which highlights the importance of event popularity prediction in public management, decision making and other security related applications. Due to the complexity of social event itself, it has two unique characteristics which most previous popularity prediction work has ignored: (1) the discussion of an event itself may consist of several components, e.g. different sub-events, different stances or different user communities; (2) the popularity of an event can be influenced by other related events. To address its unique characteristics, we propose an event popularity prediction model combining partition and interaction. We employ reinforcement learning to automatically partition an event into components and recognize related events. Then we predict event popularity by modeling component information and interactions between related events. Experimental results on a real world dataset show that our proposed model can outperform the competitive baseline methods.

Event detection for social media is an important social media analytics task in security domain, which can provide valuable information for decision making, intelligence analysis and public management. Traditional event detection methods either rely on simple term frequency based features, or take the bag of words assumption. Recently, some deep neural network based event detection methods are proposed. However, these methods still have some drawbacks. Firstly, they do not provide an effective way to learn the connection between message representation and event representation, but simply use average of message representations as the event representation. Secondly, representations in the hidden space lack interpretability compared to the traditional event keyword representation. To deal with these weaknesses, we propose an event detection approach joint learning with keyword extraction. We provide an episode learning strategy to enable the training of event representation update. In addition, by joint learning with keyword extraction, the model is more explainable, and can achieve a better performance. As the selection of a set of keywords is a combinatorial problem and non-differential, we also employ reinforcement learning in our approach. Experiments on a public available dataset show the superiority of our approach compared with baseline methods.

Online interaction behavior between web users often makes some events go viral. Popularity prediction of events is a key task in many security related applications. It forecasts how widely events would spread based on the information of evolution at an early stage. Existing methods either rely on careful feature engineering, or solely consider time series, ignoring rich information of user and text content. In this paper, we attempt to extract and fuse the rich information of text content, user and time series in a data-driven fashion. To this end, we design a popularity prediction model based on deep neural networks, which uses three encoders to extract high-level representation of text content, users and time series respectively. In addition, we incorporate attention mechanism to make our model focus on important features. Experiments on real world dataset show the effectiveness of our proposed model.

The ever-growing number of users makes social media a valuable information source about recent events. Event detection and tracking plays an important role in decision-making and public management. Despite recent progress, the performance of event detection and tracking is still limited. The majority of existing work lacks an effective way to judge whether a text related to a certain event, due to the limitations of semantic representation and heuristic similarity metric. In this paper, we present an online event detection and tracking method based on similarity metric learning using neural network. Our method first trains a classification model to identify event related texts. To detect and track events, we adopt a clustering-based approach. Specifically, we use neural network to jointly learn a similarity metric and low dimension representation of events, and then use a memory module to store and update event representation. Experiments on Twitter dataset show the effectiveness of our proposed method.

Each year in the US, 628,000 people suffer an ankle sprain, and 795,000 suffer a new or recurrent stroke. Due to improved survival rates after stroke, significant increases in stroke population are projected by 2030. So far, there is no cost-effective robotic ankle/balance trainer in the market. In this paper, we present the Virtually-Interfaced Robotic Ankle and Balance Trainer (vi-RABT), a low-cost robotic system that will improve overall ankle / balance strength, mobility and control. The system is equipped with 2 degrees of freedom (DOF) controlled actuation along with complete means of force and angular measurements. The preliminary results on a single robotic footplate confirm the system design. The system will be used for measurement of ankle kinematics, ankle kinetics and balance function, as well as for retraining motor control and strength of the ankle during plantarflexion / dorsiflexion (PF/DF), ankle inversion / eversion (IN/EV) and circumduction motions.

Social networking sites provide a convenient way for users to participate in discussion groups and communicate with others. While users situate in and enjoy such a social environment, it is important for various security related applications to understand, model and analyze participating users' behavior. In this paper, we make an attempt to model and predict user participation behavior in discussion groups of social networking sites. Our work employs a feature-based approach, which considers four types of features: thread features, content similarity, user behavior and social features. We conduct an empirical study on a popular social networking site in China, Douban.com. The experimental results show the effectiveness of our approach.

Actions are the primary way an entity interacts with other entities and acts on the external world. Action knowledge is of vital importance for behavior modeling, analysis and prediction in security informatics. In this paper, we present our approach to action knowledge extraction from Web textual data. Our approach is based on mutual bootstrapping with knowledge reasoning, which can acquire more action knowledge types and require less human participation compared with the related work. We evaluate the performance of our method and demonstrate its effectiveness through experiment.