The exponential growth of published papers in digital libraries has made finding relevant papers increasingly challenging for researchers. Numerous citation recommendation models have been developed to mitigate this issue and assist researchers in identifying pertinent works. Unfortunately, many of these models struggle to generate high-quality recommendations because they fail to capture diverse relationship patterns and effectively model the multifaceted relationships present in citation networks. Additionally, these models lack robustness in learning the representations of research papers. To address these limitations, we propose an innovative model that integrates graph embeddings to better understand hidden relationships in bibliographic networks and model multi-fold relations using the MRotatE framework. Our model first leverages graphical feature embeddings and content-based representations through MRotatE and SPECTER document embedding to construct initial representations of both query and candidate papers. These representations are then used in a generator and discriminator, which undergo adversarial training, ultimately resulting in improved recommendation outcomes. We evaluated our model against several baselines using three real-world datasets, and the results demonstrate that our approach outperforms existing models. The code for this project is publicly available online. 1

Digital publishing’s exponential growth has created vast scholarly collections. Guiding researchers to relevant resources is crucial, and knowledge graphs (KGs) are key tools for unlocking hidden knowledge. However, current methods focus on external links between concepts, ignoring the rich information within individual papers. Challenges like insufficient multi-relational data, name ambiguity, and cold-start issues further limit existing KG-based methods, failing to capture the intricate attributes of diverse entities. To solve these issues, we propose GLAMOR, a robust KG framework encompassing entities e.g., authors, papers, fields of study, and concepts, along with their semantic interconnections. GLAMOR uses a novel random walk-based KG text generation method and then fine-tunes the language model using the generated text. Subsequently, the acquired context-preserving embeddings facilitate superior top@k predictions. Evaluation results on two public benchmark datasets demonstrate our GLAMOR’s superiority against state-of-the-art methods especially in solving the cold-start problem.

Intent determination is one of the main tasks of natural language understanding and dialogue systems, aiming to determine the intent of the user’s input. Incorporating contextual information for intent determination has shown much promise. Recently, memory networks have been used to encode context from the dialogue history at each turn. However, these methods lack general mechanism for encoding intent patterns. In this paper, we investigate the incorporation of intent patterns extracted from regular expression for the multi-turn intent determination task. We propose a novel neural network model with two memories. The first memory encodes context from dialogue history at each turn whereas the second memory contains the information obtained from the regular expression patterns. We evaluate the model on Frames and Key-Value Retrieval datasets, the experimental results demonstrate that encoding intent patterns with memory networks significantly improves the multi-turn intent determination.

In this paper, we propose a neuro-fuzzy scheduler (NFS) for a multimedia Web server to ensures synchronized delivery of multimedia documents. The problem of scheduling multimedia information to ensure media synchronization in a Web environment is identified as a multicriteria scheduling problem which is NP-hard. The proposed NFS makes an intelligent compromise among multicriteria by properly combining some scheduling heuristics. Performance of the NFS is compared with several known heuristics and a branch and bound algorithm. The results show that the proposed neuro-fuzzy scheduler can dynamically adjust to the varying work-load quite well.

We consider the problem of multimedia synchronization in a Web environment. The workload generated by the multimedia server during a Web session exhibits variations that are quite different from the traffic fluctuation offered by a single media stream, e.g., a variable bit rate (VBR) video. We propose a set of parameters that can be used to characterize the workload generated by the multimedia server in a Web-type browsing environment. The workload characterization scheme is subsequently used in designing a server-based synchronization scheme. The problem of scheduling multimedia information to ensure media synchronization in a Web environment is identified as a multicriteria scheduling problem, which is NP-hard. The ability of fuzzy control to deal with multivariables makes it a good alternative for the multicriteria scheduling problem considered. Consequently, we propose a neuro-fuzzy scheduler (NFS) that makes an intelligent compromise among multicriteria by properly combining some scheduling heuristics. Performance of the NFS is compared with several known heuristics and a branch and bound algorithm. The results show that the proposed NFS ran dynamically adjust to the varying workload quite well.

In this paper we present a distributed architecture for the existing and the emerging Internet for providing synchronized virtual channels (SVCs) to support presentation of multimedia information. The SVC architecture (SVCA) provides both intra-stream and inter-stream synchronization using resources available over the Internet and implementing distributed transmission scheduling mechanisms. We also propose a set of quality of presentation (QOP) parameters that quantify the quality of multimedia presentation from the user's point of view. Based on these parameters, we evaluate the proposed architecture and provides trade-offs between the QOP parameters and the required network resources to maintain this quality. Subsequently, these trade-offs are used to generate an optimal schedule for transmission of multimedia information over the SVCA. We also present protocol mechanisms to realize the SVCA.>