Reviewerly/exHarmony huggingface dataset:
Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs

Inductive link prediction---where entities during training and inference stages can be different---has been shown to be promising for completing continuously evolving knowledge graphs. Existing models of inductive reasoning mainly focus on predicting missing links by learning logical rules. However, many existing approaches do not take into account semantic correlations between relations, which are commonly seen in real-world knowledge graphs. To address this challenge, we propose a novel inductive reasoning approach, namely TACT, which can effectively exploit Topology-Aware CorrelaTions between relations in an entity-independent manner. TACT is inspired by the observation that the semantic correlation between two relations is highly correlated to their topological structure in knowledge graphs. Specifically, we categorize all relation pairs into several topological patterns, and then propose a Relational Correlation Network (RCN) to learn the importance of the different patterns for inductive link prediction. Experiments demonstrate that TACT can effectively model semantic correlations between relations, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the inductive link prediction task.
Structured Multi-Level Interaction Network for Video Moment Localization via Language Query

We address the problem of localizing a specific moment described by a natural language query. Existing works interact the query with either video frame or moment proposal, and neglect the inherent structure of moment construction for both cross-modal understanding and video content comprehension, which are the two crucial challenges for this task. In this paper, we disentangle the activity moment into boundary and content. Based on the explored moment structure, we propose a novel Structured Multi-level Interaction Network (SMIN) to tackle this problem through multi-levels of cross-modal interaction coupled with content-boundary-moment interaction. In particular, for cross-modal interaction, we interact the sentence-level query with the whole moment while interacting the word-level query with content and boundary, as in a coarse-to-fine manner. For content-boundary-moment interaction, we capture the insightful relations between boundary, content, and the whole moment proposal. Through multi-level interactions, the model obtains robust cross-modal representation for accurate moment localization. Extensive experiments conducted on three benchmarks (i.e., CharadesSTA, ActivityNet-Captions, and TACoS) demonstrate the proposed approach outperforms the state-of-the-art methods.
Cooperative Federated Learning and Model Update Verification in Blockchain-Empowered Digital Twin Edge Networks

With the rapid development of Internet of Things (IoT), the digital twin is emerging as one of the most promising technologies to connect physical components with digital space for better optimization of physical systems. However, the limited wireless resource and security concerns impede the deployment of the digital twin in IoT. In this article, we exploit blockchain to propose a new digital twin edge networks framework for enabling flexible and secure digital twin construction. We first develop cooperative federated learning through an access point (AP) to help resource-limited smart devices in constructing digital twin at the network edges belonging to different mobile network operators (MNOs). Then, we propose a model update chain by leveraging directed acyclic graph (DAG) blockchain to secure both local model updates and global model updates. In order to incentivize the APs to help in local models training for resource-limited smart devices and also encourage the APs to contribute resource in local model update verification, we design an iterative double auction-based joint cooperative federated learning and local model update verification scheme. The optimal unified time for cooperative federated learning and local model update verification is solved to maximize social welfare. Numerical results illustrate that the proposed scheme is efficient in digital twin construction.
“My colleague is a robot” – exploring frontline employees' willingness to work with collaborative service robots

Purpose As service robots increasingly interact with customers at the service encounter, they will inevitably become an integral part of employee's work environment. This research investigates frontline employee's perceptions of collaborative service robots (CSR) and introduces a new framework, willingness to collaborate (WTC), to better understand employee–robot interactions in the workplace. Design/methodology/approach Drawing on appraisal theory, this study employed an exploratory research approach to investigate frontline employees' cognitive appraisal of service robots and their WTC with their nonhuman counterparts in service contexts. Data collection consisted of 36 qualitative problem-centered interviews. Following an iterative thematic analysis, the authors introduce a research framework of frontline employees' WTC with service robots. Findings First, this study demonstrates that the interaction between frontline employees and service robots is a multistage appraisal process based on adoption-related perceptions. Second, it identifies important attributes across three categories (employee, robot and job attributes) that provide a foundation to understand the appraisal of CSRs. Third, it presents four employee personas (supporter, embracer, resister and saboteur) that provide a differentiated perspective of how service employee–robot collaboration may differ. Practical implications The article identifies important factors that enable and restrict frontline service employees' (FSEs’) WTC with robots. Originality/value This is the first paper that investigates the appraisal of CSRs from the perspective of frontline employees. The research contributes to the limited research on human–robot collaboration and expands existing technology acceptance models that fall short to explain post-adoptive coping behavior of service employees in response to service robots in the workplace.
Augmented Reality in Precision Farming: Concepts and Applications

The amount of arable land is limited, yet the demand for agricultural food products is increasing. This issue has led to the notion of precision farming, where smart city-based technologies (e.g., Internet of Things, digital twins, artificial intelligence) are employed in combination to cater for increased production with fewer resources. Widely used in manufacturing, augmented reality has demonstrated impactful solutions for information communication, remote monitoring and increased interaction. Yet, the technology has only recently begun to find a footing alongside precision farming solutions, despite the many benefits possible to farmers through augmenting the physical world with digital objects. Therefore, this article reflects on literature discussing current applied solutions within agriculture, where augmented realty has demonstrated a significant impact for monitoring and production. The findings discuss that augmented reality must be coupled with other technologies (e.g., simultaneous localization and mapping algorithms, global positioning systems, and sensors), specifically 9 are identified across 2 application domains (livestock and crop farming) to be beneficial. Attention is also provided on how augmented reality should be employed within agriculture, where related-work examples are drawn from in order to discuss suitable hardware approaches and constraints (e.g., mobility).
Deep Learning for Weakly-Supervised Object Detection and Localization: A Survey

Weakly-Supervised Object Detection (WSOD) and Localization (WSOL), i.e., detecting multiple and single instances with bounding boxes in an image using image-level labels, are long-standing and challenging tasks in object detection. Hundreds of WSOD and WSOL methods and numerous techniques have been proposed in the deep learning era. To this end, in this paper, we consider WSOL as a sub-task of WSOD and provide a comprehensive survey of the recent achievements of WSOD. Specifically, we firstly describe the formulation and setting of the WSOD, including the background, challenges, basic framework. Meanwhile, we summarize and analyze all advanced techniques and training and test tricks for improving detection performance. Then, we introduce the widely-used datasets and evaluation metrics of WSOD. Lastly, we discuss the future directions of WSOD. We believe that these summaries can help pave a way for future research on WSOD and WSOL.
BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification

Graph-based Aspect-based Sentiment Classification (ABSC) approaches have yielded stateof-the-art results, expecially when equipped with contextual word embedding from pretraining language models (PLMs).However, they ignore sequential features of the context and have not yet made the best of PLMs.In this paper, we propose a novel model, BERT4GCN, which integrates the grammatical sequential features from the PLM of BERT, and the syntactic knowledge from dependency graphs.BERT4GCN utilizes outputs from intermediate layers of BERT and positional information between words to augment GCN (Graph Convolutional Network) to better encode the dependency graphs for the downstream classification.Experimental results demonstrate that the proposed BERT4GCN outperforms all state-of-the-art baselines, justifying that augmenting GCN with the grammatical features from intermediate layers of BERT can significantly empower ABSC models.
DEGREE: A Data-Efficient Generation-Based Event Extraction Model

Event extraction requires high-quality expert human annotations, which are usually expensive.Therefore, learning a data-efficient event extraction model that can be trained with only a few labeled examples has become a crucial challenge.In this paper, we focus on low-resource end-to-end event extraction and propose DE-GREE, a data-efficient model that formulates event extraction as a conditional generation problem.Given a passage and a manually designed prompt, DEGREE learns to summarize the events mentioned in the passage into a natural sentence that follows a predefined pattern.The final event predictions are then extracted from the generated sentence with a deterministic algorithm.DEGREE has three advantages to learn well with less training data.First, our designed prompts provide semantic guidance for DEGREE to leverage label semantics and thus better capture the event arguments.Moreover, DEGREE is capable of using additional weaklysupervised information, such as the description of events encoded in the prompts.Finally, DE-GREE learns triggers and arguments jointly in an end-to-end manner, which encourages the model to better utilize the shared knowledge and dependencies among them.Our experimental results demonstrate the strong performance of DEGREE for low-resource event extraction.* The authors contribute equally.
LRTDP Versus UCT for Online Probabilistic Planning

UCT, the premier method for solving games such as Go, is also becoming the dominant algorithm for probabilistic planning. Out of the five solvers at the International Probabilistic Planning Competition (IPPC) 2011, four were based on the UCT algorithm. However, while a UCT-based planner, PROST, won the contest, an LRTDP-based system, Glutton, came in a close second, outperforming other systems derived from UCT. These results raise a question: what are the strengths and weaknesses of LRTDP and UCT in practice? This paper starts answering this question by contrasting the two approaches in the context of finite-horizon MDPs. We demonstrate that in such scenarios, UCT's lack of a sound termination condition is a serious practical disadvantage. In order to handle an MDP with a large finite horizon under a time constraint, UCT forces an expert to guess a non-myopic lookahead value for which it should be able to converge on the encountered states. Mistakes in setting this parameter can greatly hurt UCT's performance. In contrast, LRTDP's convergence criterion allows for an iterative deepening strategy. Using this strategy, LRTDP automatically finds the largest lookahead value feasible under the given time constraint. As a result, LRTDP has better performance and stronger theoretical properties. We present an online version of Glutton, named Gourmand, that illustrates this analysis and outperforms PROST on the set of IPPC-2011 problems.
A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis

A Clinical Decision Support System (CDSS) that can amass Electronic Health Record (EHR) and other patient data holds promise to provide accurate classification and guide treatment choices. Our objective is to develop the Decision Support System for Making Personalized Assessments and Recommendations Concerning Breast Cancer Patients (DPAC), which is a CDSS learned from data that recommends the optimal treatment decisions based on a patient's features.We developed a Bayesian network architecture called Causal Modeling with Internal Layers (CAMIL), and an algorithm called Treatment Feature Interactions (TFI), which learns from data the interactions needed in a CAMIL model. Using the TFI algorithm, we learned interactions for six treatments from the LSDS-5YDM dataset. We created a CAMIL model using these interactions, resulting in a DPAC which recommends treatments towards preventing 5-year breast cancer metastasis.In a 5-fold cross-validation analysis, we compared the probability of being metastasis free in 5 years for patients who made decisions recommended by DPAC to those who did not. These probabilities are (the probability for those making the decisions appears first): chemotherapy (.938, .872); breast/chest wall radiation (.939, .902); nodal field radiation (.940, .784); antihormone (.941, .906); HER2 inhibitors (.934, .880); neadjuvant therapy (.931, .837). In an application of DPAC to the independent METABRIC dataset, the probabilities for chemotherapy were (.845, .788).Patients who took the advice of DPAC had, as a group, notably better outcomes than those who did not. We conclude that DPAC is effective at amassing and analyzing data towards treatment recommendations. Some of the findings in DPAC are controversial. For example, DPAC says that chemotherapy increases the chances of metastasis for many node negative patients. This controversy shows the importance of developing a conclusive version of DPAC to ensure we provide patients with the best patient-specific treatment recommendations.
FilterNet: Adaptive Information Filtering Network for Accurate and Fast Image Super-Resolution

Deep convolutional neural network (CNN) approaches have achieved impressive performance for image super-resolution (SR). The main issue of image SR is to effectively recover the high-frequency detail of low-resolution (LR) input. However, existing CNN methods often inevitably exhibit a large amount of memory consumption and computational cost. In addition, in most SR networks, the low-frequency and high-frequency components of the LR features are treated equally in the training process, which can ignore the local detailed information and hinder the representational capacity of networks. To solve these issues, in this paper, we propose a deep adaptive information filtering network (FilterNet) for accurate and fast image SR. In contrast to the existing methods that adopt fully CNN methods to directly predict the HR images, the proposed FilterNet concentrates on more useful features and adaptively filters the redundant low-frequency information. In general, we present the dilated residual group (DRG), which consists of multiple dilated residual units. The DRGs can directly expand the receptive field of the network to efficiently exploit the contextual information of the LR input. In the dilated residual unit, a gated selective mechanism is proposed to adaptively learn more high-frequency information and filter the low-frequency information. Besides, we introduce a novel adaptive information fusion structure, which builds long scaling skip connections among the DRGs to rescale the hierarchical features and fuse more detailed information. The scaling weights can be deemed as the part parameters of our network and trained adaptively. The Extensive evaluations on benchmark datasets demonstrate that our FilterNet achieves superior performance both on accuracy and speed compared with recent state-of-the-art methods.
A survey on evaluation of summarization methods

The increasing volume of textual information on any topic requires its compression to allow humans to digest it. This implies detecting the most important information and condensing it. These challenges have led to new developments in the area of Natural Language Processing (NLP) and Information Retrieval (IR) such as narrative summarization and evaluation methodologies for narrative extraction. Despite some progress over recent years with several solutions for information extraction and text summarization, the problems of generating consistent narrative summaries and evaluating them are still unresolved. With regard to evaluation, manual assessment is expensive, subjective and not applicable in real time or to large collections. Moreover, it does not provide re-usable benchmarks. Nevertheless, commonly used metrics for summary evaluation still imply substantial human effort since they require a comparison of candidate summaries with a set of reference summaries. The contributions of this paper are three-fold. First, we provide a comprehensive overview of existing metrics for summary evaluation. We discuss several limitations of existing frameworks for summary evaluation. Second, we introduce an automatic framework for the evaluation of metrics that does not require any human annotation. Finally, we evaluate the existing assessment metrics on a Wikipedia data set and a collection of scientific articles using this framework. Our findings show that the majority of existing metrics based on vocabulary overlap are not suitable for assessment based on comparison with a full text and we discuss this outcome.
End-to-end Deep Reinforcement Learning Based Coreference Resolution

Recent neural network models have significantly advanced the task of coreference resolution.However, current neural coreference models are typically trained with heuristic loss functions that are computed over a sequence of local decisions.In this paper, we introduce an end-to-end reinforcement learning based coreference resolution model to directly optimize coreference evaluation metrics.Specifically, we modify the state-of-the-art higherorder mention ranking approach in Lee et al. ( 2018) to a reinforced policy gradient model by incorporating the reward associated with a sequence of coreference linking actions.Furthermore, we introduce maximum entropy regularization for adequate exploration to prevent the model from prematurely converging to a bad local optimum.Our proposed model achieves new state-of-the-art performance on the English OntoNotes v5.0 benchmark.
A Test Detecting the Outliers for Continuous Distributions Based on the Cumulative Distribution Function of the Data Being Tested

One of the pillars of experimental science is sampling. Based on the analysis of samples, estimations for populations are made. There is an entire science based on sampling. Distribution of the population, of the sample, and the connection among those two (including sampling distribution) provides rich information for any estimation to be made. Distributions are split into two main groups: continuous and discrete. The present study applies to continuous distributions. One of the challenges of sampling is its accuracy, or, in other words, how representative the sample is of the population from which it was drawn. To answer this question, a series of statistics have been developed to measure the agreement between the theoretical (the population) and observed (the sample) distributions. Another challenge, connected to this, is the presence of outliers - regarded here as observations wrongly collected, that is, not belonging to the population subjected to study. To detect outliers, a series of tests have been proposed, but mainly for normal (Gauss) distributions—the most frequently encountered distribution. The present study proposes a statistic (and a test) intended to be used for any continuous distribution to detect outliers by constructing the confidence interval for the extreme value in the sample, at a certain (preselected) risk of being in error, and depending on the sample size. The proposed statistic is operational for known distributions (with a known probability density function) and is also dependent on the statistical parameters of the population—here it is discussed in connection with estimating those parameters by the maximum likelihood estimation method operating on a uniform U(0,1) continuous symmetrical distribution.
An Improved Squirrel Search Algorithm for Optimization

Squirrel search algorithm (SSA) is a new biological-inspired optimization algorithm, which has been proved to be more effective for solving unimodal, multimodal, and multidimensional optimization problems. However, similar to other swarm intelligence-based algorithms, SSA also has its own disadvantages. In order to get better global convergence ability, an improved version of SSA called ISSA is proposed in this paper. Firstly, an adaptive strategy of predator presence probability is proposed to balance the exploration and exploitation capabilities of the algorithm. Secondly, a normal cloud model is introduced to describe the randomness and fuzziness of the foraging behavior of flying squirrels. Thirdly, a selection strategy between successive positions is incorporated to preserve the best position of flying squirrel individuals. Finally, in order to enhance the local search ability of the algorithm, a dimensional search enhancement strategy is utilized. 32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are used to test the global search ability of the proposed ISSA. Experimental test results indicate that ISSA provides competitive performance compared with the basic SSA and other four well-known state-of-the-art optimization algorithms.
Graph Intention Network for Click-through Rate Prediction in Sponsored Search

Estimating click-through rate (CTR) accurately has an essential impact on improving user experience and revenue in sponsored search. For CTR prediction model, it is necessary to make out user's real-time search intention. Most of the current work is to mine their intentions based on users' real-time behaviors. However, it is difficult to capture the intention when user behaviors are sparse, causing thebehavior sparsity problem. Moreover, it is difficult for user to jump out of their specific historical behaviors for possible interest exploration, namelyweak generalization problem. We propose a new approach Graph Intention Network (GIN) based on co-occurrence commodity graph to mine user intention. By adopting multi-layered graph diffusion, GIN enriches user behaviors to solve the behavior sparsity problem. By introducing co-occurrence relationship of commodities to explore the potential preferences, the weak generalization problem is also alleviated. To the best of our knowledge, the GIN method is the first to introduce graph learning for user intention mining in CTR prediction and propose end-to-end joint training of graph learning and CTR prediction tasks in sponsored search. At present, GIN has achieved excellent offline results on the real-world data of the e-commerce platform outperforming existing deep learning models, and has been running stable tests online and achieved significant CTR improvements.