Reviewerly/exHarmony huggingface dataset:
Learning Semantic-Rich Relation-Selective Entity Representation for Knowledge Graph Completion

Many existing knowledge graph embedding methods learn semantic representations for entities by using graph neural networks (GNN) to harvest their intrinsic relevances. However, these methods mostly represent every entity with one coarse-grained representation, without considering the variation of the semantics of an entity under the context of different relations. To tackle this problem, we propose a method to learn multiple representations of an entity, with each emphasizing one specific aspect of information contained in the entity. During training and testing, only the representation that is most relevant to the considered relation is selected and shown to the model, leading to the relation-selective representations. To enable the selection of representations according to the relation, we first propose to incorporate a relation-controlled gating mechanism into the original GNN, which is used to decide which and how much information can flow into the next updating stage of the GNN. Then, a mixture of relation-level and entity-level negative sample generation methods is further developed to enhance semantic information contained in relation-selective entity representations under the framework of contrastive learning. Experiments on three benchmarks show that our proposed model outperforms all strong baselines.
Modeling High-Order Relation to Explore User Intent with Parallel Collaboration Views

As an emerging paradigm, session-based recommendation (SBR) aims to predict the next item by exploiting user behaviors within a short yet anonymous session. Existing works focus on how to effectively model the information based on graph neural networks, which may be insufficient to capture the high-order relation for short-term interest. To this end, we propose a novel framework, named PacoHGNN, which models high-order relations based on HyperGraph Neural Network with Parallel Collaboration views. Specifically, PacoHGNN learns two embedding views for the SBR task, respectively: (i) item-internal graph view, which is to learn the item embedding by modeling pairwise item connectivities among corresponding items; and (ii) session-external hypergraph view, which targets session embedding by learning beyond pairwise information from high-order relations across all sessions. These two types of graph modeling with data-driven can provide complementary information for each other while exhibiting collaboration to some degree. Additionally, we further propose Hyperedge-to-Node (H2N) to enhance supervised signals against the data sparsity problem for better graph representation. Extensive experiments on multiple real-world datasets demonstrate the superiority of the proposed model over state-of-the-art methods.
Adaptive Rounding Compensation for Post-training Quantization

Network quantization can compress and accelerate deep neural networks by reducing the bit-width of network parameters so that the quantized networks can be deployed to resource-limited devices. Post-Training Quantization (PTQ) is a practical method of generating a hardware-friendly quantized network without re-training or fine-tuning. However, PTQ results in unacceptable accuracy degradation due to disturbance caused by clipping and discarding the rounded remains. To address this problem, we propose Adaptive Rounding Compensation Quantization (ARCQ) to reduce the quantization errors by utilizing the rounded remains and clipping threshold that can be computed in resource-limited devices. Moreover, to leverage accuracy and speed, we propose a dynamic compensation method to select critical layers to be compensated in terms of parameters and quantization errors. Extensive experiments verify that our method can achieve superior results on ImageNet for classification and MSCOCO for object detection. Codes are available at https://github.com/Iconip2022/ARCQ .
Research on Answer Generation for Chinese Gaokao Reading Comprehension

Answering questions in a university’s entrance examination like Gaokao in China challenges AI technology. In this paper, we focus on answer generation task in QA of Chinese reading comprehension in Gaokao, and propose a method that combines the pre-trained model CPT and Integer Linear Programming. First, our method employs CPT to retrieve answer sentences that containing important information. Secondly, the sentences output by the CPT are optimized by introducing various constraints through Integer Linear Programming. Experiments on question answering demonstrate the proposed model obtains substantial performance gains over various neural model baselines in terms of multiple evaluation metrics.
Attentive Hawkes Process Application for Sequential Recommendation

Sequential recommendation systems which aim to predict the users' future clicking items are essential in both research and industry services. Based on the contextual characteristics of users, a large number of existing methods exploit abundant information from user behavior sequences and gain users' interests through expressive sequential models like recurrent neural networks and self-attention mechanism to predict the next item. However, the temporal generative process of behavior sequences, which is crucial to the complex relationships and distributions of users' dynamic preferences, is rarely considered. In this paper, we propose HawRec (Hawkes process based sequential recommendation), which is a new representation learning approach to model the interacted sequences of users from a temporal point process perspective. In particular, we leverage temporal self-attention mechanism to make an analogy to the self-exciting mechanism of Hawkes process. We bring expressive superiority of neural network into the Hawkes process with non-parametric conditional intensity kernel, resulting in more accurate recommendation and inheriting robustness from the temporal point process. Furthermore, extensive experiments conducted on four public benchmarks demonstrate that our HawRec outperforms various baselines of state-of-the-art sequential recommendation.
Revisiting Positive and Negative Samples in Variational Autoencoders for Top-N Recommendation

Top-N recommendation is a common tool to discover interesting items, which ranks the items based on user preference using their interaction history. Implicit feedback is often used by recommender systems due to the hardness of preference collection. Recent solutions simply treat all interacted items of a user as equally important positives and annotate all no-interaction items of a user as negatives. We argue that this annotation scheme of implicit feedback is over-simplified due to the sparsity and missing fine-grained labels of the feedback data. To overcome this issue, we revisit the so-called positive and negative samples for Variational Autoencoders (VAEs). Based on our analysis and observation, we propose a self-adjusting credibility weight mechanism to re-weigh the positive samples and exploit the higher-order relation based on item-item matrix to sample the critical negative samples. Besides, we abandon complex nonlinear structure and develop a simple yet effective VAEs framework with linear structure, which combines the reconstruction loss function for the positive samples and critical negative samples. Extensive experiments conducted on 4 public real-world datasets demonstrate that our VAE++ outperforms other VAEs-based models by a large margin.
ASLEEP: A Shallow neural modEl for knowlEdge graph comPletion

Knowledge graph completion aims to predict missing relations between entities in a knowledge graph. One of the effective ways for knowledge graph completion is knowledge graph embedding. However, existing embedding methods usually focus on combined models, variant deep neural networks, or additional information, which inevitably increase computational complexity and are unfriendly to real-time applications. In this paper, we take a step back and propose a novel shallow neural network model for knowledge graph completion. Specifically, given an entity pair, our model first extracts features of head and tail entities through linear transformations. Then entity features are integrated into an entity-pair representation via a max operation followed by a non-linear transformation. Finally, according to the entity-pair representation, our model calculates probability of each relation through multi-label modeling to predict relations for the given entity pair. Experimental results over two widely used datasets show that our model outperforms the baseline methods. The source code of this paper can be obtained from https://github.com/Joni-gogogo/KBC-ASLEEP .
Block Decomposition with Multi-granularity Embedding for Temporal Knowledge Graph Completion

Temporal knowledge graph (TKG) completion is the mainstream method of inferring missing facts based on existing data in TKG. Majority of existing approaches to TKG focus on embedding the representation of facts from a single-faceted low-dimensional space, which cannot fully express the information of facts. Furthermore, most of them lack the comprehensive consideration of both temporal and non-temporal facts, resulting in the inability to handle the two types of facts simultaneously. Thus, we propose BDME, a novel Block Decomposition with Multi-granularity Embedding model for TKG completion. It adopts multivector factor matrices and core tensor em-bedding for fine-grained representation of facts based on the principle of block decomposition. Moreover, it captures interaction information between entities, relationships, and timestamps in multiple dimensions. By further constructing a temporal and static interaction model, BDME processes temporal and non-temporal facts in a unified manner. Besides, we propose two kinds of constraint schemes, which introduce time embedding angle and entity bias component to avoid the overfitting problem caused by a large number of parameters. Experiments demonstrate that BDME achieves sub-stantial performance against state-of-the-art methods on link prediction.
Hyperbolic Mutual Learning for Bundle Recommendation

Bundle recommendation aims to accurately predict the probabilities of user interactions with bundles. Most existing effective methods learn the embeddings of users and bundles from user-bundle interaction view and user-item-bundle interaction view. However, they seldom leverage the recommendation difference caused by the distinct learning trends of two views when modeling user preferences. Meanwhile, such two view interaction graphs are typically tree-like. If the graph data with this structure is embedded in Euclidean space, it will lead to severe distortion problem. To this end, we propose a novel Hyperbolic Mutual Learning model for Bundle Recommendation (HyperMBR). The model encodes the entities (user, item, bundle) of the two view interaction graphs in hyperbolic space to learn their accurate representations. Furthermore, a mutual distillation based on hyperbolic distance is proposed to encourage the two views to transfer knowledge for increasingly improving the recommendation performance. Extensive empirical experiments on two real-world datasets confirm that our HyperMBR achieves promising results compared to state-of-the-art bundle recommendation methods.
Analogy-Triple Enhanced Fine-Grained Transformer for Sparse Knowledge Graph Completion

Sparse problem is the major challenge in knowledge graph completion. However, existing knowledge graph completion methods utilize entity as the basic granularity, and face the semantic under-transfer problem. In this paper, we propose an analogy-triple enhanced fine-grained sequence-to-sequence model for sparse knowledge graph completion. Specifically, the entities are first split into different levels of granularity, such as sub-entity, word, and sub-word. Then we extract a set of analogy-triples for each entity-relation pair. Furthermore, our model encodes and integrates the analogy-triples and entity-relation pairs, and finally predicts the sequence of missing entities. Experimental results on multiple knowledge graphs show that the proposed model can achieve better performance than existing methods, especially on sparse entities.
Distantly Supervised Entity Linking with Selection Consistency Constraint

Entity linking (EL) aims to find entities that the textual mentions refer to from a knowledge base (KB). The performance of current distantly supervised EL methods is not satisfactory under the condition of low-quality candidate generation. In this paper, we consider the scenario where multiple KBs are available, and for each KB, there is an EL model corresponding to it. We propose the selection consistency constraint (SCC), that is, for one sample, the entities selected from multiple KBs should be consistent if these selections are all correct. In this work, we aim to utilize the SCC to improve the performance of each EL model (not the combination of multiple EL models) under low-quality candidate generation. Specifically, we define an SCC model from two different aspects: minimizing probability and upper bound, which are used to introduce the SCC into the training of EL models. The experimental results show that our method, jointly training multiple EL models with the SCC model, outperforms the baseline which trains multiple EL models separately, and it has low cost.
Spatio-Temporal Position-Extended and Gated-Deep Network for Next POI Recommendation

The next point of interest (POI) recommendation uses the user's check-in information on location-based social networks to make recommendations. The existing methods based on deep learning are evident in improving the performance of the recommendation model by capturing users' interests and preferences. However, the methods based on recurrent neural networks ignore the dependencies of non-continuous POIs for understanding users' behaviour under spatio-temporal factors. Most attention-based methods focus on the global POI sequence, which pays attention to all POIs in the users' check-in sequences, even if some attention has very little weight. To tackle these problems, we propose a novel spatio-temporal model based on the position-extended algorithm and gated-deep network (i.e., ST-PEGD) for next POI recommendation. Specifically, by combining spatio-temporal factors, we design a gated-deep network to capture the long-term behavioral dependencies of users by generating auxiliary binary gates. In addition, when capturing the short-term behaviour dependence of users, we use the position-extended algorithm to make the contextual interaction of RNNs more sufficient when performing POI sequence hopping selection. Extensive experiments on two real datasets prove that our model performs significantly better than state-of-the-art methods.
ML-KGCL: Multi-level Knowledge Graph Contrastive Learning for Recommendation

The knowledge graph-based (KG-based) recommender systems have achieved excellent results in the recommendation domain. However, the long-tail issue hinders the model from mining the real interests of users. Existing research has shown that Contrastive Learning (CL) can alleviate the long-tail issue, but the existing graph contrastive learning methods are not completely compatible with KG-based recommendation. To fill this gap, we propose a Multi-Level Knowledge Graph Contrastive Learning framework (ML-KGCL) to introduce CL into the KG-based recommendation. ML-KGCL makes the CL task more compatible with the recommendation task while mitigating the long-tail issue by performing fine-grained node representation learning. Firstly, we generate positive samples via graph augmentation strategy. Then, we divide the KG-based recommendation into three levels: user-level, entity-level and user-item-level, and perform fine-grained multi-level CL to optimize the node representations. Next, we obtain the final node representations through the signal integration strategy. Finally, the model is trained by the joint learning paradigm. The experimental results on three public datasets are better than the baseline models.
TRAINING A NEURAL NETWORK TO LEARN THE INVERSE KINEMATIC MAPPING OF A RHINO ROBOT

Off-line programming of robots requires the determination of the joint coordinate angles. However, inverse kinematic solutions for robot control and positioning are intended for the "ideal" robot and, therefore, do not take into account the robot's inherent deficiencies. These deficiencies include inaccuracies in the arm-element dimensions, internal play of joints, internal non-linearities of gearing, deflection of arm elements, and servo-positioning errors. Also, traditional approaches to solve such problems are computationally intensive and require frequent calibration to maintain positional accuracy. In this paper we report a technique that uses a neural network to learn the idiosyncrasies of a robot. In particular, a multi-layer feed-forward network is trained by the back propagation algorithm to learn the inverse kinematic solution that is unique to the Rhino robot used here.
HIT - An Effective Approach to Build a Dynamic Financial Knowledge Base

In recent years, due to their expertise and comprehensiveness in a specific domain, domain-specific knowledge bases (KBs) have attracted more and more attention from both academics and industries. Among these domain-specific KBs, financial KBs have become more and more popular and valuable due to their broad spectrum of downstream applications, such as quantitative investment analysis, financial risk analysis, and financial domain-based KBQA. However, due to their massive volume, high conflicts, and frequent volatile properties, it is pretty challenging to build an error-prone dynamic financial KB. To address these challenges, in this paper, we propose a dynamic financial KB construction pipeline that mainly consists of two fundamental modules, a Human-Interacted (HI) distant supervised evolved relation extraction module targets at obtaining the evolved knowledge with less manual annotations and high extraction accuracy, and a Temporal (T) duplication and conflict resolution module focus on applying a data fusion algorithm to the knowledge fusion task to select high-confidence knowledge without duplication and conflict by incorporating the temporal information. Through extensive experiments, we have demonstrated the effectiveness of HIT. Compared to state-of-the-art solutions, HIT can improve the accuracy by $$10.6\%$$ on average for the relation extraction task and by $$6.9\%$$ on average for the duplication and conflict resolution task, respectively.
A DYNAMIC-B MODEL OF LEARNING

We present a new learning model which accounts for changing values of b, a phenomenon found in the learning process. It is shown that this new model can be expressed in terms of two parameters, given the standard time for the task. The Power Curve turns out to be a particular case of the dynamic <i>b</i> model.<br> Checking the model for empirical data yields better fits than the Power Curve. Two methods for finding the model's parameters are presented.<br> We believe the dynamic-<i>b</i> model can provide important insights into the mechanics of the learning process and improve predictive capability of completion times. All this is achieved by building a model which is compatible with empirical findings such as a changing <i>b</i> and a nonzero standard time.
CONTROL ISSUES FOR INTEGRATED DESIGN AGENTS

The concurrent engineering process demands new capabilities from integrated design systems. In the future, no longer will data-driven only integration solutions be acceptable. Instead, strategic control aspects will have to be taken into account as well. This paper provides an approach based on Fuzzy Logic for the control of integrated design agents. The focus is on the definition of suitable fuzzy sets and rules for control. The concept is being realized using the blackboard-based paradigm as a framework for the system implementation.
TOWARDS COLLABORATIVE VIRTUAL PROTOTYPING IN A WORLD MARKET

Virtual prototyping is a new technology that is considered strategically important by many manufacturers and research institutes. To be competitive in the world market, new products with rapidly increasing complexity have to be developed. In order to foster a close cooperation with branch offices or subcontractor design information has to be exchanged faster and more easier. New networking technologies together with communication and conferencing services promote workspace collaboration. This paper provides a brief overview of virtual prototyping, computer supported collaborative work (CSCW) services, network technologies, discusses some facts with respect to collaboration, and draws some conclusions about the impact of an collaborative virtual prototyping environment.
Class-Dynamic and Hierarchy-Constrained Network for Entity Linking

Entity Linking (EL) aims to map mentions in a text to corresponding entities in a knowledge base. Existing EL methods usually rely on sufficient labeled data to achieve the best performance. However, the massive investment in data makes EL systems viable only to a limited audience. There is ample evidence that introducing entity types can provide the model prior knowledge to maintain the model performance in low-data regimes. Unfortunately, current low-data EL methods usually employ entity types by rule constraints, which are in a shallow manner. Furthermore, they usually ignore fine-grained interaction between mention and its context, resulting in insufficient semantic information of mention representation in low-data regimes. To this end, we propose a Class-Dynamic and Hierarchy-Constrained Network (CDHCN) for entity linking. Specifically, we propose a dynamic class scheme to learn a more effective representation for each entity type. Besides, we formulate a hierarchical constraint scheme to reduce the matching difficulty of the given mention and corresponding candidate entities by utilizing entity types. In addition, we propose an auxiliary task called mention position prediction (MPP) to obtain an informative mention representation in low-data regimes. Finally, extensive in-domain and out-of-domain experiments demonstrate the effectiveness of our method.
Combining Autoencoder with Adaptive Differential Privacy for Federated Collaborative Filtering

Recommender systems provide users personalized services by collecting and analyzing interaction data, undermining user privacy to a certain extent. In federated recommender systems, users can train models on local devices without uploading raw data. Nevertheless, model updates transmitted between the user and the server are still vulnerable to privacy inference attacks. Several studies adopt differential privacy to obfuscate transmitted updates, but they ignore the privacy sensitivity of recommender model components. The problem is that components closer to the original data are more susceptible to privacy leakage. To address this point, we propose a novel adaptive privacy-preserving method combining autoencoder for federated collaborative filtering, which guarantees privacy meanwhile maintaining high model performance. First, we extend the variational autoencoder (VAE) to federated settings for privacy-preserving recommendations. Additionally, we analyze the privacy risks of the variational autoencoder model in federated collaborative filtering. Subsequently, we propose an adaptive differential privacy method to enhance user privacy further. The key is to allocate less privacy budget for sensitive layers. We apply a metric based on model weights to determine the privacy sensitivity of each layer in the autoencoder. Then we adaptively allocate the privacy budget to the corresponding model layer. Extensive experiments and analysis demonstrate that our method can achieve competitive performance to non-private recommender models meanwhile providing fine-grained privacy protection.