Reviewerly/exHarmony huggingface dataset:
Chinese Relation Extraction of Apple Diseases and Pests Based on BERT and Entity Information

Most existing methods for Chinese relation extraction suffer from fine-grained relation categories and unbalanced category distribution in the field of apple diseases and pests. To solve above problems, we construct the AppleRE dataset, which contains 28 relation categories and 20060 relation instances with the characteristic of richer categories than existing agricultural datasets. Then, we propose a relation extraction model BE-ARE introducing BERT and entity information, in which dynamic character representations and entities that reflects the unique meaning of Chinese words are utilized to enhance the data features. The performance of BE-ARE on AppleRE achieved a precision of 98.44%, a recall of 96.75% and an F1-score of 97.59%. The F1- score is increased by 1.77%–12.38%, which outperforms the comparison models. Experimental results demonstrate the competitiveness of BE-ARE when considering fine-grained classification and unbalanced category distribution. In addition, the proposed model shows the generalization on the public datasets in distinct domains.
Elastic Container Scheduling for Stochastically Arrived Workflows in Cloud and Edge Computing

Many intelligent applications of Cloud and Edge can be modeled to be stochastically arrived workflows. Elastically provisioning resources such as containers to these applications is crucial to minimize resource costs while fulfilling deadlines. Most of existing workflow scheduling algorithms are either not for randomly arrived workflows from users of Edge Computing or only consider workflows in pure Cloud Computing. In this article, an elastic resource scheduling method, which integrates loosely coupled workflow scheduling with resource auto-scaling, is developed for stochastically arrived workflows in Cloud and Edge to minimize long-term operating costs while meeting deadline constraints. Experimental results illustrate that our approach achieves a lower overall cost and a higher success ratio compared with the state-of-art algorithm.
Relationship Structure Analysis on Determinants of Enterprise Information Security Investment Decision Based on ISM

Information security is one of the biggest threats to enterprises; how to identify the determinants of enterprise information security investment (EISI) and put forward corresponding countermeasures is related to the continuity of enterprise operation, which is an urgent problem to be solved at present. This article identifies the index system of factors that affect the EISI by using the Delphi method. The interpretive structural model (ISM) of the influence factors of EISI is constructed. The result shows that the ISM of EISI determinants is a multilevel hierarchical structure with five levels. According to the logical structure of the model, the most direct reasons affecting EISI are the level of enterprise information security technology and enterprise information security behavior; information security laws and regulations and national policies are the deep-seated reasons for EISI. It is of theoretical value and reference to understand the formation, model establishment, and evolution mechanism of EISI decision making.
Named Entity Recognition in Biology Literature Based on Unsupervised Domain Adaptation Method

By the careful rearrangement and analysis, various meaningful information could be extracted from the published biological literature, which is of great significance for the related follow-up research. Since the rapid accumulation of literature publications, manual-based curation method is too inefficient to cope with the massive biological literature data. Knowledge extraction methods in computer science seem to be able to process the biology literature more efficiently, however, most of them are based on supervised learning and greatly limited by the annotation quality of the species corpora. Here we present a new named entity recognition algorithm named biolitNER, which features unsupervised domain adaptation and accordingly has the capability to recognize named entities on biomedical literature across domains. Considering the lack of well-annotated corpora of many species, biolitNER is of great utility for named entity recognition and bears critical significance for biomedical literature curation in various species. Experimental verification shows that, compared with traditional programs, biolitNER produces higher quality results and has a satisfactory runtime performance.
A Bayesian Approach to Inference on Probabilistic Surveys

We propose a nonparametric Bayesian approach for conducting inference on probabilistic surveys. We use this approach to study whether U.S. Survey of Professional Forecasters density projections for output growth and inflation are consistent with the noisy rational expectations hypothesis. We find that in contrast to theory, for horizons close to two years, there is no relationship whatsoever between subjective uncertainty and forecast accuracy for output growth density projections, both across forecasters and over time, and only a mild relationship for inflation projections. As the horizon shortens, the relationship becomes one-to-one, as the theory would predict.
Hypergraph-Based Academic Paper Recommendation

Academic paper recommendation aims to provide personalized recommendation services for scholars from massive academic papers. Deep Learning-based Collaborative Filtering plays an important role in it, and most of existing method are based on bipartite graph, which causes it fail to realize multi-features fusion, and the over-smooth property of GCN limits the generation of embedding with high-order similarity, resulting in the decline of recommendation quality. In this paper, we propose a hypergraph-based academic paper recommendation method. Based on hypergraph, APRHG (Academic Paper Relation HyperGraph) is constructed to not only model the complex academic relationship between users and papers, but also realize the multi-features fusion. In addition, the L-HGCF (Light HyperGraph based Collaborative Filtering) algorithm, which could mine high-order similarity between papers, is proposed to provide trusted recommendations. We conduct experiments on the public dataset, and compare the performance with several deep learning based Collaborative Filtering to confirm the superiority of our method.
Fall Detection Using Omnidirectional Camera

Falling is one of the crucial problems threatening health of the elderly. It is especially dangerous when the elderly lives alone. Using omnidirectional cameras instead of traditional pinhole cameras in surveillance systems has become an attractive choice recently, as they provide a wider field of view. However, images captured by omnidirectional camera are highly distorted, especially in peripheral areas. Therefore, it is required to modify surveillance approaches that are developed for conventional pinhole cameras to adapt with such omnidirectional cameras. In this paper, a fall detection method using omnidirectional camera is proposed. First, blobs corresponding to moving objects are detected directly from the omnidirectional video by background subtraction method. Then different blob’s features based on its contour and minimum bounding rectangle are extracted to classify state of the blob as fall or not fall. Experiments on BOMNI omnidirectional video dataset show that the highest accuracy of 97.07% when using KNN as background subtraction method and classifier.
A GAT-Based Chinese Text Classification Model: Using of Redical Guidance and Association Between Characters Across Sentences

Cognitive psychology research has shown that humans tend to use objects in the abstract real world as cognition. For the Chinese in particular, the first thing to emerge from the process of writing formation is pictographs. This feature is very useful for Chinese text classification. Fortunately, many basic and extended radical-related systems are also included in all Chinese dictionaries. Moreover, for Chinese texts the graph attention structure can better record the information of features in Chinese texts. To this end, we propose a GAT- based Chinese text classification model considering character, word, sentence position information, character position information and radicals as nodes in Chinese text. The model is based on Redical guidance and association between Characters across Sentences (RCS). In order to better exploit the common features of Chinese characters and sequence features in text, this paper designs a model with context-awareness, which is capable of collecting information at a distance. Finally, we conduct extensive experiments, and the experimental results not only prove the superiority of our model, but also verify the effectiveness of the radical and GAT model in Chinese text classification tasks.
IMDb30: A Multi-relational Knowledge Graph Dataset of IMDb Movies

Most knowledge graph embedding (KGE) models are trained and evaluated through common benchmark datasets such as WN18 and FB15k. However, these datasets belong to the general filed and have been utilized as link prediction benchmarks for many years. In addition, some of them suffer from test leakage, thus cannot evaluate KGE models effectively. To provide a new link prediction benchmark of field-specific knowledge graph without test leakage, we proposed a new dataset called IMDb30, which incorporate knowledge of IMDb (Internet Movie Database) movies. We construct IMDb30 based on the public relational data released on IMDb website. The complete IMDb30 contains more than 6 million triplets, and a subset of IMDb30 is also constructed to conduct experiments. IMDb30 subset contains 115080 triplets formed by 31343 entities and 30 relations. We conduct link prediction experiments for 3 convolutional neural network models of KGE on the subset and the results show that IMDb30 can effectively train and evaluate KGE models. The complete dataset and the construction process are made publicly available.
TSC-GCN: A Face Clustering Method Based on GCN

For face clustering, the density of each cluster distribution in the feature space is different. Too high similarity pruning will lead to the sparse clustering not being divided and reducing the recall ratio, while too low similarity pruning will lead to the decline of clustering accuracy. We propose the Two-Stage Clustering Method Based on Graph Convolutional Neural Network (TSC-GCN), in which the clustering size are set to measure the sparse degree of clustering and pruning with a low similarity degree. The clustering with sparse distribution is screened out, then the requirements for nodes similarity are improved. At the same time, the number of neighbor nodes is set to prevent the clustering core from deviating from the aggregation of nodes, and the clustering with dense distribution is screened out. The experimental results show that TSC-GCN can give a good consideration of the accuracy and recall ratio both, and achieve better clustering effectiveness than the state-of-the-art methods.
Signal Embeddings for Complex Logical Reasoning in Knowledge Graphs

Complex logical reasoning over Knowledge Graph is one of the fundamental tasks of Artificial Intelligence. Traditional approaches suffer from the incompleteness and noise of knowledge graph, making complex logical reasoning a challenging task. Recent methods propose to embed entities and first-order logic (FOL) queries in low-dimensional vector spaces. However, most of the current models cannot deal with logical negation. In addition, many methods utilize neural networks to model relation projections, which require a large number of parameters and computational expense. In this work, we proposes SignalE that simplifies relation projection operations while being able to handle logical negation. We represent entities and queries by signal embeddings, which can be represented in both time domain and frequency domain interchangeably. The logical negation operation is handled by inverting the amplitude in each dimension of the frequency-domain form of the signal embedding. Furthermore, relational projection operations are simplified into translation between entities. Experiments demonstrate that SignalE significantly outperforms existing state-of-the-art methods on benchmark datasets.
Domino Saliency Metrics: Improving Existing Channel Saliency Metrics with Structural Information

Channel pruning is used to reduce the number of weights in a Convolutional Neural Network (CNN). Channel pruning removes slices of the weight tensor so that the convolution layer remains dense. The removal of these weight slices from a single layer causes mismatching number of feature maps between layers of the network. A simple solution is to force the number of feature map between layers to match through the removal of weight slices from subsequent layers. This additional constraint becomes more apparent in DNNs with branches where multiple channels need to be pruned together to keep the network dense. Popular pruning saliency metrics do not factor in the structural dependencies that arise in DNNs with branches. We propose Domino metrics (built on existing channel saliency metrics) to reflect these structural constraints. We test Domino saliency metrics against the baseline channel saliency metrics on multiple networks with branches. Domino saliency metrics improved pruning rates in most tested networks and up to 25% in AlexNet on CIFAR-10.
Concept Maps as a Means of Providing Methodology Orientation of General Pedagogical Training of Pre-service Teachers

The article presents a general description of the means of providing methodology orientation of general pedagogical training of pre-service teachers and scientific and pedagogical workers. Theoretical and methodological principles of using concept maps in the process of general pedagogical training of prospective specialists in education, scientific approaches to the essence of the notions «concept», «conceptualization», the main provisions of the cognitive theory have been characterised. The semantic and functional characteristics of concept maps, the common and the different between concept maps and intelligence maps have been revealed. The stages of drawing up concept maps have been characterized. The peculiarities of concept maps as a means of providing methodology orientation of general pedagogical training of pre-service teachers. The factors that influence the effectiveness of the use of concept maps in the educational process of higher education have been identified. Examples of concept maps in the context of providing methodology orientation of general pedagogical training of pre-service teachers and scientific and pedagogical workers have been given.
Mario Fast Learner: Fast and Efficient Solutions for Super Mario Bros

Super Mario Bros (SMB) are popular video games. Reinforcement learning has solved various problems including robot control and the game of Go. This article focuses on reinforcement learning methods for Super Mario Bros (SMB) games. Previous methods could solve all available SMB single player open source levels by using reinforcement learning methods. The article summarizes that previous evaluation metrics include reward function, loss function and the arrival of the endpoint flag but these metrics cannot fully judge the quality of the policies. The article analyzes the difficulties for agents to complete SMB levels and points out the problems that need to be solved. To solve the problems, the article proposes a new judging metric for SMB games called 100 recent accuracy. The article propose a solution to speed up the training procedure and improve the experimental results. According to the experimental results, the new solution has good experimental performance under the new evaluation metrics proposed in this article.
Pre-train Unified Knowledge Graph Embedding with Ontology

Existing knowledge graph embedding models mainly focus on a single task, such as link prediction or entity typing, which actually cannot ensure the generalization capability of the model. Recent research shows that introducing additional ontology information can naturally convert the entity typing task to a specific case of link prediction between the instance and ontology layers. However, the unbalanced scale of the two layers brings difficulty for learning. To this end, we pre-train the knowledge graph embedding on the instance and schema layers of KG respectively on the basis of Rot-Pro, a model that is capable to express the transitivity relation pattern occurred in class hierarchy of the ontology. Furthermore, we construct a dataset by integrating entity type and class hierarchy information based on YAGO3 for evaluating the model efficiency on both link prediction and entity typing tasks. Experimental result shows that our model provided a unified and effective approach for both tasks.
Text-Enhanced and Relational Context Based Hyperbolic Knowledge Graph Embedding

Knowledge Graphs (KGs) are ubiquitous structures for information storage and increasingly becoming popular for a variety of downstream task, and KGs are often incomplete and knowledge graph embedding learn low-dimensional representations of entities and relations, which can be used for knowledge graph completion task. However, there still exists some problems. The important role of relational context which includes neighbor relations, head and tail entities is often overlooked in many existing methods. Entity semantic information contained in textual description of entities is not well used. Thus, we propose Text Enhanced Relational Context Hyperbolic Graph Convolution Network (TRHGCN) for knowledge graph completion. TRHGCN model utilize the hyperbolic space, which can better excavate the hierarchical structure of knowledge graph, and makes full use of text description and relational context to represent entities and relations in knowledge graph. Link prediction experiments are carried out on FB15K-237, WN18RR, NELL-995 datasets. We also do ablation studies on relational context and text description of entities. The results demonstrate that TRHGCN significantly outperforms the latest excellent methods such as CompGCN and ConvE, and our model performs better on hierarchical relations.
Illiquid Bitcoin Options

This paper conducts a first look into the regulated Bitcoin options market in the United States. Compared to stock options, bitcoin options tend to be ten times more illiquid as measured by bid-ask spreads. The illiquidity significantly affects bitcoin options pricing: Given that investors are on average net sellers of bitcoin options, heightened illiquidity is associated with a significant premium in subsequent delta-hedged returns, which also strengthens under more imbalanced investor orders. To support the reasonings behind our findings, we further exploit a policy change that allows retail participation and significantly influences order imbalances in the Bitcoin options market.
Word Sense Disambiguation Based on Memory Enhancement Mechanism

Word sense disambiguation (WSD) is a very critical yet challenging task in natural language processing (NLP), which aims at identifying the most suitable meaning of ambiguous words in the given contexts according to a predefined sense inventory. Existing WSD methods usually focus on learning the semantic interactions between a special ambiguous word and the glosses of its candidate senses and thus ignore complicated relations between the neighboring ambiguous words and their glosses, leading to insufficient learning of the interactions between words in context. As a result, they are difficult to leverage the knowledge from the other ambiguous words which might provide some explicit clues to identify the meaning of current ambiguous word. To mitigate this challenge, this paper proposes a novel neural model based on memory enhancement mechanism for WSD task, which stores the gloss knowledge of previously identified words into a memory, and further utilizes it to assist the disambiguation of the next target word. Extensive experiments, which are conducted on a unified evaluation framework of the WSD task, demonstrate that our model achieves better disambiguation performance than the state-of-the-art approaches (Code: https://github.com/baoshuo/WSD ).
Prompt as a Knowledge Probe for Chinese Spelling Check

Chinese Spelling Check (CSC) is a challenging task to detect and correct wrong characters in Chinese sentences. Since most Chinese spelling mistakes are caused by visual or pronunciation similarities of characters, recent researches tend to utilize external phonological and morphological resources for this task. However, their works rely heavily on hand-constructed confusion sets and multimodal data, causing high labor costs. To this end, we propose an end-to-end generative model called PromptCSC. First, we notice that the misspelling of characters causes unnatural semantic incoherence in sentences. By using the prompt template as a knowledge probe, PromptCSC detects and outputs the error probability of each character in the sentence. The error locations are then corrected using BERT’s soft mask mechanism. Experimental results on the SIGHAN benchmarks show that our approach achieves excellent performance without external resources.
Better than Xml: Towards a Lexicographic Markup Language

This paper takes a critical look at how XML is used in lexicography and asks the question, why do dictionary entries often end up looking so complex when encoded in XML? The main reason for the perceived complexity of XML-encoded dictionaries is purely structural markup: XML elements which contain other XML elements (instead of humanreadable text). The over-abundance of purely structural content in lexicography is caused by the nature of lexicographic content, much of which is inherently headed. XML has no support for headedness, but in this paper we propose an alternative markup language which does.
Cross-CAM: Focused Visual Explanations for Deep Convolutional Networks via Training-Set Tracing

In recent years, the widely used deep learning technologies have always been controversial in terms of reliability and credibility. Class Activation Map (CAM) has been proposed to explain the deep learning models. Existing CAM-based algorithms highlight critical portions of the input image, but they don’t go any farther in tracing the neural network’s decision-basis. This work proposes Cross-CAM, a visual interpretation method which supports deep traceability for prediction-basis samples and focuses on similar regions of the category based on the input image and the prediction-basis samples. The Cross-CAM extracts deep discriminative feature vectors and screens out the prediction-basis samples from the training set. The similarity-weight and the grad-weight are then combined to form the cross-weight, which highlights similar regions and aids in classification decisions. On the ILSVRC-15 dataset, the proposed Cross-CAM is tested. The new weakly-supervised localization evaluation metric IoS (Intersection over Self) is proposed to effectively evaluate the focusing effect. Using Cross-CAM highlight regions, the top-1 location error for weakly-supervised localization achieves 44.95% on the ILSVRC-15 validation set, which is 16.25% lower than Grad-CAM. In comparison to Grad-CAM, Cross-CAM focuses on the key regions using the similarity between the test image and the prediction-basis samples, according to the visualisation results.
Supporting Trustworthy Artificial Intelligence via Bayesian Argumentation

This paper introduces argumentative-generative models for statistical learning—i.e., generative statistical models seen from a Bayesian argumentation perspective—and shows how they support trustworthy artificial intelligence (AI). Generative Bayesian approaches are already very promising for achieving robustness against adversarial attacks, a fundamental component of trustworthy AI. This paper shows how Bayesian argumentation can help us achieve transparent assessments of epistemic uncertainty and testability of models, two necessary ingredients for trustworthy AI. We also discuss the limitations of this approach, notably those traditionally linked to Bayesian methods.