Biomedical information extraction (BioIE) is an important task. The aim to analyze biomedical texts and extract structured such as named entities semantic relations between them. In recent years, pre-trained language models have largely improved the performance of BioIE. However, they neglect incorporate external structural knowledge, which can provide rich factual support underlying understanding reasoning for extraction. this paper, we first evaluate current methods, including vanilla...

While recently Multimodal Large Language Models (MM-LLMs) have made exciting strides, they mostly fall prey to the limitation of only input-side multimodal understanding, without ability produce content in multiple modalities. As we humans always perceive world and communicate with people through various modalities, developing any-to-any MM-LLMs capable accepting delivering any modality becomes essential human-level AI. To fill gap, present an end-to-end general-purpose MM-LLM system,...

Variety and veracity are two distinct characteristics of large-scale heterogeneous data. It has been a great challenge to efficiently represent process big data with unified scheme. In this paper, tensor model is proposed the unstructured, semistructured, structured With extension operator, various types represented as subtensors then merged tensor. order extract core which small but contains valuable information, an incremental high singular value decomposition (IHOSVD) method presented. By...

With the advent of ubiquitous sensing and networking, future social networks turn into cyber-physical interactions, which are attached with associated attributes. Therefore, network analysis is advancing interconnections among cyber, physical, spaces. Community detection an important issue in analysis. Users a usually have some interactions their friends community because common interests or similar profiles. In this paper, efficient algorithm k-clique using formal concept (FCA) - typical...

Document-level relation extraction aims to detect the relations within one document, which is challenging since it requires complex reasoning using mentions, entities, local and global contexts.Few previous studies have distinguished explicitly, may be problematic because they play different roles in intra-and inter-sentence relations.Moreover, interactions between contexts should considered could help based on our observation.In this paper, we propose a novel mention-based (MRN) module...

Accurate cotton maps are crucial for monitoring growth and precision management. The paper proposed a county-scale mapping method by using random forest (RF) feature selection algorithm classifier based on selecting multi-features, including spectral, vegetation indices, texture features. contribution of features to classification accuracy was also explored in addition spectral index. In addition, the optimal time, importance, best extraction were evaluated. results showed that named gray...

Social recommendation has been popular and successful in various urban sustainable applications such as online sharing, products shopping services. These allow users to form several implicit social networks through their daily interactions. The can rate some interesting items give comments. majority of the existing studies have investigated rating prediction based on user-item bipartite graph user-user graph, so called recommendation. However, spatial factor was not considered mechanisms....

Mobile social networks (MSNs) facilitate connections between mobile users and allow them to find other potential who have similar interests through devices, communicate with them, benefit from their information. As MSNs are distributed public virtual spaces, the available information may not be trustworthy all. Therefore, often at risk since they any prior knowledge about others socially connected. To address this problem, trust inference plays a critical role for establishing links in MSNs....

Disease prediction based on Electronic Health Records (EHR) has become one hot research topic in biomedical community. Existing work mainly focuses the of target disease, and little is proposed for multiple associated diseases prediction. Meanwhile, a piece EHR usually contains two main information: textual description physical indicators. However, existing largely adopts statistical models with discrete features from numerical indicators EHR, fails to make full use information. In this...

Mobile social networks (MSNs) provide real-time information services to individuals in communities through mobile devices. However, due their high openness and autonomy, MSNs have been suffering from rampant rumors, fraudulent activities, other types of misuses. To mitigate such threats, it is urgent control the spread fraud information. The research challenge is: how design strategies efficiently utilize limited resources meanwhile minimize individuals' losses caused by information? this...

Mobile social networks (MSNs) have become an indispensable way for people to access information, express emotions, and communicate with each other. However, the advent extensive use of MSNs has also created fertile soil breeding rapid spread rumors. Therefore, blocking rumors in always been a hot topic this field. With idea crowdsourcing, we propose novel rumor control framework, called Crowdblocking, which users can implement schemes collaborative distributed way, so that be controlled more...

Mobile CrowdSensing (MCS) has emerged as a novel paradigm for performing large-scale sensing tasks. Many incentive mechanisms have been proposed to encourage user participation in MCS. However, most of them ignore the inevitable cold start stage MCS, where MCS system just begun releasing Also, they all adopt single-round without considerations continuous cumulative effect. Given severe shortage participants this paper proposes Multi-Round Incentive Mechanism (MRIM). MRIM is based on monetary...

Abstract The amount of movie has increased to become more congested; therefore, find a what users are looking for through the existing technologies very hard. For this reason, want system that can suggest requirement them and best technology about these is recommendation system. However, most using collaborative filtering methods predict needs user due method gives accurate prediction. Today, many researchers paid attention develop several improve accuracy rather than methods. Hence, further...

Thanks to the booming development of artificial intelligence, 5G technology, and intelligent manufacturing numerous edge devices contained in industrial Internet Things (IIoT) are endowed with ability mine knowledge from perceived massive data. Knowledge-driven IIoT plays an unprecedented role application fields such as cyber-physical systems Industry 4.0. However, is generally scattered across distributed IIoT. Therefore, order further achieve intelligence IIoT, it very important explore...

The capacity to create “fake” videos has recently raised concerns about the reliability of multimedia content. Identifying between true and false information is a critical step toward resolving this problem. On issue, several algorithms utilizing deep learning facial landmarks have yielded intriguing results. Facial are traits that solely tied subject’s head posture. Based on observation, we study how Head Pose Estimation (HPE) patterns may be utilized detect deepfakes in work. HPE studied...

Language Models (LMs) have demonstrated impressive molecule understanding ability on various 1D text-related tasks. However, they inherently lack 2D graph perception — a critical of human professionals in comprehending molecules’ topological structures. To bridge this gap, we propose MolCA: Molecular Graph-Language Modeling with Cross-Modal Projector and Uni-Modal Adapter. MolCA enables an LM (i.e., Galactica) to understand both text- graph-based molecular contents via the cross-modal...

Text-to-video (T2V) synthesis has gained increasing attention in the community, which recently emerged diffusion models (DMs) have promisingly shown stronger performance than past approaches. While existing state-of-the-art DMs are competent to achieve high-resolution video generation, they may largely suffer from key limitations (e.g., action occurrence disorders, crude motions) with respect intricate temporal dynamics modeling, one of crux synthesis. In this work, we investigate...