Reviewerly/exHarmony huggingface dataset:
AIR: Adaptive Incremental Embedding Updating for Dynamic Knowledge Graphs

Recently, knowledge graphs (KGs) have been successfully applied on various downstream tasks, such as online recommendation and question answering. To better utilize the knowledge in KGs, recent researchers propose to learn low-dimension embeddings for entities and relations in KGs. Unlike static KGs, dynamic KGs evolve with knowledge events. Therefore, the embeddings of entities and relations should be updated to capture the information of latest knowledge events. However, when new events arrive, existing approaches update the whole knowledge graph or only update limited neighbors of entities directly involved in events. These approaches cannot maintain high-quality KG embedding in an efficient way. In this paper, we propose a framework AIR to adaptively update the embeddings for dynamic KGs. Specifically, AIR first measures the importance score of each triple. Then, AIR selects a set of triples that are most affected by knowledge events for updating. Besides, we apply the embedding propagation method to update the embedding to avoid retraining. Therefore, our proposed AIR can efficiently maintain high-quality KG embeddings. Extensive experiments on four real-world dynamic KGs demonstrate the effectiveness and efficiency of AIR against state-of-the-art baselines.
Multi-view Spatial-Temporal Enhanced Hypergraph Network for Next POI Recommendation

Next point-of-interest (POI) recommendation has been a prominent and trending task to provide next suitable POI suggestions for users. Current state-of-the-art studies have achieved considerable performances by modeling user-POI interactions or transition patterns via graph- and sequential-based methods. However, most of them still could not well address two major challenges: 1) Ignoring important spatial-temporal correlations during aggregation within user-POI interactions; 2) Insufficiently uncovering complex high-order collaborative signals across users to overcome sparsity issue. To tackle these challenges, we propose a novel method Multi-View Spatial-Temporal Enhanced Hypergraph Network (MSTHN) for next POI recommendation, which jointly learns representations from local and global views. In the local view, we design a spatial-temporal enhanced graph neural network based on user-POI interactions, to aggregate and propagate spatial-temporal correlations in an asymmetric way. In the global view, we propose a stable interactive hypergraph neural network with two-step propagation scheme to capture complex high-order collaborative signals. Furthermore, a user temporal preference augmentation strategy is employed to enhance the representations from both views. Extensive experiments on three real-world datasets validate the superiority of our proposal over the state-of-the-arts. To facilitate future research, we release the codes at https://github.com/icmpnorequest/DASFAA2023_MSTHN .
Towards Efficient and Effective Transformers for Sequential Recommendation

Transformer and its variants have been intensively applied for sequential recommender systems nowadays as they take advantage of the self-attention mechanism, feed-forward network (FFN) and parallel computing capability to generate the high-quality sequence representation. Recently, a wide range of fast, efficient Transformers have been proposed to facilitate sequence modeling, however, the lack of a well-established benchmark might lead to the non-reproducible and even inconsistent results across different works, making it hard to gain rigorous assessments. In this paper, We provide a benchmark for reproducibility and present a comprehensive empirical study on various Transformer-based recommendation approaches, and key techniques or components in Transformers. Based on this study, we propose a hybrid effective and Efficient Transformer variant for sequential Recommendation (ETRec), which incorporates the scalable long- and short-term preference learning, blocks of items aggregating as interests, and parameter-efficient cross-layer sharing FFN. Extensive experiments on six public benchmark datasets demonstrate the advanced efficacy of the proposed approach.
A Machine-Reading-Comprehension Method for Named Entity Recognition in Legal Documents

Named Entity Recognition (NER) is essential for helping people quickly grasp legal documents. To recognise nested and non-nested entities in legal documents, in this paper, we propose a Machine-Reading-Comprehension (MRC) method, which is integrated with biaffine attention and graph-based dependency parsing. Specifically, we regard an NER task as a $$\langle Query, Context, Answer \rangle $$ triple and construct a query statement for each entity according to the annotation guideline notes. Then, we use the BERT pre-trained language model to encode Query and Context, fused with the rotary position embedding after feature mapping. Next, we use the biaffine attention to score each subsequence of a text. Finally, we use a balanced softmax to decide whether or not a subsequence is an entity. We do many experiments to show that our model has achieved good results in recognising nested and non-nested entities. We also do some experiments to demonstrate the effectiveness of some components in the entire model we propose.
BiQCap: A Biquaternion and Capsule Network-Based Embedding Model for Temporal Knowledge Graph Completion

Temporal Knowledge Graphs (TKGs) provide a temporal context for facts, capturing temporal information and the dynamic nature of actual world facts. However, typical TKGs often suffer from incomplete dynamics with missing facts in real-world scenarios. Temporal Knowledge Graph Embedding (TKGE) is one of the critical approaches to tackling the challenge. However, the existing TKGE models are weak in simultaneously representing hierarchical semantics and other relation patterns. Therefore, embedding TKGs in a single space, no matter the Euclidean space, or hyperbolic space, cannot capture the complex structures of TKGs accurately. In addition, few existing models have a "deep" architecture for modeling the entries in a quadruple at the same dimension. In this paper, we propose a new TKGE model, $$\textbf{BiQCap}$$ , which for the first time, combines biquaternion and capsule network in modeling to make up for the defects of existing TKGE models. BiQCap represents each temporal entity as a translation and each relation as euclidean rotation and hyperbolic rotation in biquaternions vector space. Further, we employ the embeddings of entities, relations, and temporal trained from biquaternions as the input to capsule networks. Experimental results on five well-known benchmark datasets show that our BiQCap achieves state-of-the-art performance.
Dual-View Self-supervised Co-training for Knowledge Graph Recommendation

Knowledge Graph Recommendation (KGR), which aims to incorporate Knowledge Graphs (KGs) as auxiliary information into recommender systems and effectively improve model performance, has attracted considerable interest. Currently, KGR community has focused on designing Graph Neural Networks (GNNs)-based end-to-end KGR models. Unfortunately, existing GNNs-based KGR models are focused on extracting high-order attributes (knowledge) but suffer from restrictions in several vital aspects, such as 1) neglect of finer-grained feature interaction information via GNNs and 2) lack of adequate supervised signals, leading to undesirable performance. To tackle these gaps, we propose a novel Dual-view Self-supervised Co-training for Knowledge Graph Recommendation (DSCKG). We consider two different views, covering user-item collaborative view and KGs structural view. Precisely, for the collaborative view, we first extract high-order collaborative user/item representations with GNNs. Next, we impose a discrepancy regularization term to augment the self-discrimination of the user/item representations. As for the structural view, we initially utilize GNNs to extract high-order features. Next, we utilize novel Dual-core Convolutional Neural Networks to extract bit- and vector-level finer-grained feature interaction signals. DSCKG hence performs a high-quality self-supervised co-training paradigm across dual views, improving the node representation learning capability. Experimental results demonstrate DSCKG achieves remarkable improvements over SOTA methods.
PIDE: Propagating Influence of Dynamic Evolution on Interaction Networks for Recommendation

Modelling dynamic interactions between users and items is very crucial for many recommendation systems. Although existing methods have achieved great success in modelling dynamic interactions, they ignore propagating influence from interacting users/items to their neighbors along different paths at the same time. In this paper, we focus on how to effectively propagate influence of dynamic evolution on interaction networks. Specifically, we propose a new influence-enhanced deep co-evolutionary method called PIDE, which captures not only mutual influence between the interacting users and items but also propagation influence from them to their neighbors along high-order connectivity. Our model consists of two main components: interaction mutual influence component and influence propagation component. The former evolves the effective representations for the interacting user and item by considering the direct mutual influence from each other when a new interaction happens. The latter refines the representations of users and items via aggregating the effective interaction influence propagated from multiple interacting nodes along different high-order connectivity at the same time. Finally, the refined representations of users and items are used to predict which item the user is most likely to interact with. To the best of our knowledge, this is the first attempt to propagate multiple interaction influences at the same time on the dynamic network for better recommendation. The experimental results illustrate that our method significantly outperforms the state-of-the-art methods on three real-world datasets (Our codes and datasets are available at: https://github.com/pidecode/PIDE.git .).
Rewriting-Stego: Generating Natural and Controllable Steganographic Text with Pre-trained Language Model

Data transmission security and privacy play a crucial role in the era of information technology. Although the widely-used data encryption technique can ensure security, it can be easily detected and blocked by the observation system because the encrypted data format is quite different from the normal data. This work focuses on linguistic steganography, hiding a secret text in another normal stego text to ensure security and decrease the risk of being detected simultaneously. Rather than following the existing edit-based or generation-based paradigm, we propose a novel rewriting-based Rewriting-Stego, which tries to hide a secret text in the stego text by rewriting the given cover text. This paradigm integrates the advantages of both the edit-based paradigm and the generation-based paradigm, bringing higher information capacity without losing naturalness and controllability. Extensive experimental results on three public datasets have demonstrated the effectiveness of our Rewriting-Stego in terms of multiple metrics.
Anole: A Lightweight and Verifiable Learned-Based Index for Time Range Query on Blockchain Systems

Time range query is essential to facilitate a wide range of blockchain applications such as data provenance in the supply chain. Existing blockchain systems adopt the storage-consuming tree-based index structure for better query performance, however, fail to efficiently work for most blockchain nodes with limited resources. In this paper, we propose Anole, a lightweight and verifiable time range query mechanism, to present the feasibility of building up a learned-based index to achieve high performance and low storage costs on blockchain systems. The key idea of Anole is to exploit the temporal characteristics of blockchain data distribution and design a tailored lightweight index to reduce storage costs. Moreover, it uses a digital signature to guarantee the correctness and completeness of query results by considering the learned index's error bounds, and applies batch verification to further improve verification performance. Experimental results demonstrate that Anole improves the query performance by up to 10 $$\times $$ and reduces the storage overhead by $$99.4\%$$ compared with the state-of-the-art vChain+.
Efficient and Accurate Range Counting on Privacy-Preserving Spatial Data Federation

A spatial data federation is a collection of data owners (e.g., a consortium of taxi companies), and collectively it could provide better location-based services (LBS). For example, car-hailing services over a spatial data federation allow end users to easily pick the best offers. We focus on the range counting queries, which are primitive operations in spatial databases but received little attention in related research, especially considering the privacy requirements from data owners, who are reluctant to disclose their proprietary data. We propose a grouping-based technical framework named FedGroup, which groups data owners without compromising privacy, and achieves superior query accuracy (up to 50% improvement) as compared to directly applying existing privacy mechanisms achieving Differential Privacy (DP). Our experimental results also demonstrate that FedGroup runs orders-of-magnitude faster than traditional Secure Multiparty Computation (MPC) based method, and FedGroup even scales to millions of data owners, which is a common setting in the era of ubiquitous mobile devices.
Towards Defending Against Byzantine LDP Amplified Gain Attacks

Local differential privacy (LDP) has been widely used to collect sensitive data from distributed users while preserving individual privacy. However, very recent studies show that LDP is vulnerable to manipulation and poisoning attacks. Maximal gain attack (MGA) is one of the most fundamental examples. In this paper, we take one step further to introduce a novel type of attacks called Byzantine LDP amplified gain attacks (BLAGA) that is precisely derived from the randomness of an LDP protocol, unveiling LDP's inherent conflict between privacy and security. We show that MGA is a special case of BLAGA. Subsequently, we propose a defense framework that makes use of a data-driven approach to automatically identify the target items via multi-round data collection. It differs from existing solutions in that it does not require any prior knowledge, which is normally difficult to acquire in practical settings. Finally, we perform extensive experiments on various datasets to show that our defense framework can well preserve the utility of heavy hitter identification with effective security protection.
CB-GAN: Generate Sensitive Data with a Convolutional Bidirectional Generative Adversarial Networks

In the era of big data, numerous data measurements collected from all walks of life are playing important roles in various data mining applications. Not all data owners (or keepers) could develop feasible learning models for knowledge discovery's sake. Oftentimes, the original data need to be passed to or shared with researchers or data scientists for better mining insights, especially in the medical, financial, and industrial fields. However, concerns about sensitivity and privacy limit the availability and completeness of shared (or passed) data and the quality of mining results. In this paper, we propose a novel Convolutional Bidirectional Generative Adversarial Networks (CB-GAN) framework to generate sensitive synthetic data. The Convolutional Neural Networks are utilized to capture the feature correlations of the original data, and the Generative Adversarial Networks with Autoencoders are combined to synthesize realistically distributed data. To demonstrate the feasibility of the model, we evaluated it from three aspects: how similar are the distributions of the synthetic data to the original data, how well can the synthetic data accomplish future data mining tasks, and how much sensitive information has been hidden. Various experimental results showed the superiority of the proposed method compared with the state-of-the-art methods.
Project-based learning in training IT-personnel for the digital economy

The purpose of the study is to modernize IT personnel training to enhance the development of the digital economy by means of project-based learning methods. The objectives of the study are to harmonize the educational process with the needs of production and to develop appropriate educational and methodological support. The experience for the preparation of IT-bachelors at Lobachevsky State University of Nizhny Novgorod is analyzed on the example of the Sergach sugar factory modernization. The analysis of the experience gained at Lobachevsky State University of Nizhny Novgorod in applying the project approach shows the high potential of project-based training methods for IT specialists to work in the digital economy. In order for the project approach to become an effective means of training personnel to work in line with modern standards, it is necessary that the topics of projects be closely related to the actual problems facing real manufacturers. Therefore, a stable interaction between education and industry must be established. Project-based training methods using the above principles were introduced into the educational process of preparing bachelors in the area “Fundamental Informatics and Information Technology” at Lobachevsky State University of Nizhny Novgorod.
A Weakly Supervised Method With Colorization for Nuclei Segmentation Using Point Annotations

Nuclei segmentation is an essential step in the automatic analysis of histopathology images. This segmentation task requires much work to manually generate accurate pixel-level annotations for fully supervised training. To overcome such expensive and hard-to-get pixel-level annotations, in this paper we propose a weakly supervised method for nuclei segmentation only using point annotations based on convolutional neural network. The proposed method effectively combines a weakly supervised segmentation task and an auxiliary colorization task. The dual input with boundaries and color information maximizes the inherent features of the image. Two types of coarse labels generated from point annotations are applied to provide constraint information for the segmentation task. As an auxiliary task, colorization is incorporated to guide the network to extract effective features and improve the segmentation performance of the network. We evaluate our proposed method on two public nuclei segmentation datasets. The experimental results indicate that our method is superior to other state-of-the-art methods.
Traversable Frontiers Based Autonomous Exploration Strategy for Deploying MAVs in Subterranean Environments

Ahstract- Exploration and mapping of unknown environments is a fundamental task in applications for autonomous robots. In this article, we present an exploration-planning strategy for deploying autonomous MAVs in completely unknown areas. In exploration, the robot computes the next-best safe look-ahead poses such that by navigating to the future pose, the robot will acquire more information about the environment. The proposed strategy uses a novel frontier selection method that also contributes to the safe navigation of autonomous robots in obstructed areas such as Subterranean caves and mines. In order to compute safe look-ahead poses for the robot, the framework associates costs on a traversable frontier selection with minimal violation heading change from a most unknown direction. The proposed exploration framework is also adaptive to computational resources available on board the robot which means the trade-off between the speed of exploration and the quality of the map can be made. Such capability allows the proposed framework to be deployed in subterranean exploration, mapping as well as in fast search and rescue scenarios irrespective of the type of robot used. The performance of the proposed framework is evaluated in detailed simulation studies with comparisons made against state-of-the-art high-level exploration-planning framework as it will be presented in this article.
Research on Intelligent Question Answering Framework of Open Education based on Knowledge Graph

As a new type of adult open education oriented university, the high dropout rate of The Open University of China(OUC) has been puzzling the development of open education. The number of teachers in the Open University is small, the proportion of teachers and students is too low, and it is impossible to give students instant feedback. The main reason why students drop out of school is that online learning is difficult to get timely tutoring. Artificial intelligence provides an effective way to solve the problems in the development of education. The paper explores the use of knowledge graphs, natural language processing and other technologies to build an intelligent question answering system framework in the field of open education, including the construction of an open education knowledge base and intelligent question answering modules. By embedding the intelligent question and answer module into the learning space of OUC, so that learners can obtain timely knowledge in the learning process, it can not only improve learners ‘ interest in learning, but also promote the construction of a learning society.
Outperformance of Mall-Receptionist Android as Inverse Reinforcement Learning is Transitioned to Reinforcement Learning

Robots can tackle human–robot interaction (HRI) tasks through inverse reinforcement learning (IRL). However, offline IRL agents' performance is upper-bounded by experts. Limited demonstration fails to provide an overall picture of the environment, especially in real-world applications. To further enhance IRL's performance, we implement a cross-modal inverse reinforcement learning to reinforcement learning (IRL-to-RL) transition framework for a real-world HRI interaction task, in which a mall receptionist android promotes sanitizer usage. During the 10-day experiment, the android develops a more proactive and effective strategy than the human expert. Furthermore, we explore four decay modes of prior knowledge supervision and suggest a preferable pattern for practical use. Our results demonstrate the feasibility of the framework to assist robots in switching to diverse modalities, learning incrementally with a sparse reward function, and eventually outperforming the human expert. We anticipate our framework to inspire more IRL-to-better learning paradigms, facilitating robots to outcompete teachers in more real-world HRI applications.
DBSAGAN: Dual Branch Split Attention Generative Adversarial Network for Super-Resolution Reconstruction in Remote Sensing Images

Super-resolution reconstruction methods emerge in an endless stream, but the models proposed by many researchers are not fit for certain types of images, such as remote sensing images. This is because remote sensing images have rich texture details and geometrical structures. Therefore, directly applying previous models to remote sensing images generates unsatisfactory artifacts. In this letter, we propose a dual branch split attention generative adversarial network (DBSAGAN) for super-resolution tasks on remote sensing images. Specifically, the proposed DBSAGAN adopts a dual branch split attention group as the cascading basic unit in the generator. In addition, we remove batch normalization layers in the basic unit to improve the generative ability of the network. To reduce the gap between the reconstructed and original images from the frequency domain, we innovatively use focal frequency loss to constrain the network. Experiments demonstrate that the proposed network outperforms existing state-of-the-art methods on the Gaofen-1 remote sensing image dataset.