Reviewerly/exHarmony huggingface dataset:
Design of an Ontology Concept Semantic Representation Model for Schema Layer of Geographic Knowledge Graphs

Knowledge graphs have great application value in fields of accurate search, auxiliary decision-making and natural language question answering systems. The distributed vector representation of knowledge is an important research direction of knowledge graph. A good knowledge representation model is very important for several tasks of knowledge graph such as entity relation extraction, knowledge complement and knowledge fusion. In this paper, a neural network model is proposed to obtain the semantic vector representation of ontology concepts in a geographic knowledge graph. The model simulates human brain’s understanding, memory and association of abstract geographic concepts, and expresses the concept as a weighting of several concepts closely related to it. Firstly, the semantic relation vector representation model between concepts is obtained by using a multi-layers convolutional neural network superposition of self attention mechanism. The first-order nonlinear relationship and high-order coupling relationship between different features of the same concept as well as different concepts are considered in the model. The contribution weight of surrounding concepts to the central concept is determined through training procedure. By using the model proposed in this paper, we can train the vector representation of the ontology concept of geographic knowledge graphs, embed the schema layer of knowledge graph into a vector space, and provide support for the relation extraction of geographical entities and the fusion of different knowledge graphs.
The End of Surprise? The State of the Subject of Secondary English and Implications for Initial Teacher Education

The chance to be part of educational experiences which provide dialogic spaces for the surprising or unexpected are essential for future English teachers and their pupils. The impact of neoliberal reforms on the secondary English curriculum and initial teacher education restricts teachers' autonomy and pedagogical creativity whereby teaching approaches are reduced to approved processes, leading to monologic and repetitive learning experiences for young people. This has begun a process which, as philosopher Martin Buber would argue, denies the possibility of spontaneity and meaningful expression in the classroom. In response, this paper reflects on my position as a former secondary English teacher and recently employed lecturer in initial teacher education. I set out a way forward where beginning teachers can be empowered to achieve a balance between external expectations and their own values in relation to their subject and the pedagogical approaches they employ. To achieve this, we need to provide spaces for critical reflection on so-called 'best practice' in teaching and learning, especially for those at the beginning of their career.
Adversarial Deep Embedded Clustering: On a better trade-off between Feature Randomness and Feature Drift (Extended abstract)

Deep clustering models are trained based on self-supervision and pseudo-supervision. However, applying these techniques can cause Feature Randomness and Feature Drift. On one hand, Feature Randomness takes place when a considerable portion of the pseudo-labels do not match the true ones. On the other hand, Feature Drift takes place when there is a strong con-flict between the self-supervision and pseudo-supervision tasks. We propose ADEC (Adversarial Deep Embedded Clustering) a novel autoencoder-based clustering model, which relies on a discriminator network to reduce random features while avoiding the drifting effect. Experimental results validate that our model alleviates these problems and outperforms existing methods.
MAMDR: A Model Agnostic Learning Framework for Multi-Domain Recommendation

Large-scale e-commercial platforms in the real-world usually contain various recommendation scenarios (domains) to meet demands of diverse customer groups. Multi-Domain Recommendation (MDR), which aims to jointly improve recommendations on all domains and easily scales to thousands of domains, has attracted increasing attention from practitioners and researchers. Existing MDR methods usually employ a shared structure and several specific components to respectively leverage reusable features and domain-specific information. However, data distribution differs across domains, making it challenging to develop a general model that can be applied to all circumstances. Additionally, during training, shared parameters often suffer from domain conflict while specific parameters are inclined to overfitting on data sparsity domains. In this paper, we first present a scalable MDR platform served in Taobao that enables to provide services for thousands of domains without specialists involved. To address the problems of MDR methods, we propose a novel model agnostic learning framework, namely MAMDR, for the multi-domain recommendation. Specifically, we first propose a Domain Negotiation (DN) strategy to alleviate the conflict between domains. Then, we develop a Domain Regularization (DR) to improve the generalizability of specific parameters by learning from other domains. We integrate these components into a unified framework and present MAMDR, which can be applied to any model structure to perform multi-domain recommendation. Finally, we present a large-scale implementation of MAMDR in the Taobao application and construct various public MDR benchmark datasets which can be used for following studies. Extensive experiments on both benchmark datasets and industry datasets demonstrate the effectiveness and generalizability of MAMDR.
Eventfulness for Interactive Video Alignment

Humans are remarkably sensitive to the alignment of visual events with other stimuli, which makes synchronization one of the hardest tasks in video editing. A key observation of our work is that most of the alignment we do involves salient localizable events that occur sparsely in time. By learning how to recognize these events, we can greatly reduce the space of possible synchronizations that an editor or algorithm has to consider. Furthermore, by learning descriptors of these events that capture additional properties of visible motion, we can build active tools that adapt their notion of eventfulness to a given task as they are being used. Rather than learning an automatic solution to one specific problem, our goal is to make a much broader class of interactive alignment tasks significantly easier and less time-consuming. We show that a suitable visual event descriptor can be learned entirely from stochastically-generated synthetic video. We then demonstrate the usefulness of learned and adaptive eventfulness by integrating it in novel interactive tools for applications including audio-driven time warping of video and the extraction and application of sound effects across different videos.
Learning Deep Graph Representations via Convolutional Neural Networks (Extended abstract)

R-convolution graph kernels are positive-semidefinite functions that decompose graphs into substructures and compare them. One problem in the effective implementation of this idea is that the substructures are not independent, which leads to high-dimensional feature space. In addition, graph kernels cannot capture the high-order complex interactions between vertices. To mitigate these two problems, we propose a framework called DeepMap to learn deep representations for graph feature maps used in graph kernels. The learned deep representation for a graph is a dense and low-dimensional vector that captures complex high-order interactions in a vertex neighborhood. We empirically validate DeepMap on various graph classification benchmarks and demonstrate that it achieves state-of-the-art performance.
Retracted: Sustainable Development Research of Green Smart Park in High-End Manufacturing Based on Internet of Things

[This retracts the article DOI: 10.1155/2022/3961647.].
Retracted: Promoting Regional Economic Transformation Forecast Based on Intelligent Computing Technology

[This retracts the article DOI: 10.1155/2022/1835376.].
About Python

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Topic detection based on BERT and seed LDA clustering model

Aiming at the problem that the LDA model is not effective for short text topic extraction, this paper proposes a topic detection method based on BERT and seed LDA clustering model. Firstly, the seed LDA model (sLDA) is designed for optimize the LDA model. A seed word set is introduced for the LDA model. The words generated under a certain probability will be generated from the seed word set, thus guiding topic generation; Secondly, sLDA and BERT were combined to construct the B-sLDA model. The sLDA model was used to obtain topic features, and the BERT model was used to obtain text features. The result of fusing two features were input into the K-means clustering model to obtain topic clusters. Finally, to complete the topic detection , each cluster was input into the TF-IDF model, and the first 10 output words were used as the topic of the corresponding cluster. In this paper, the official dataset 20 newsgroups and the real public opinion dataset about "Shanghai COVID-19" obtained by crawler were selected for the experiment. The experimental results show that sLDA is superior to the original LDA model in terms of perplexity and coherence, which proves that the seed word set has a certain guiding effect on the LDA model. B-sLDA clustering model has significant improvement in Silhouette Coefficient and Calinski-Harabasz.
An Enhanced Visual Object Tracking Approach using dual tree complex wavelet transform and Hierarchical Convolutional Features

This paper study introduces a novel technique to enhance object tracking algorithms by utilizing dual tree complex wavelet transform for extracting hierarchical convolutional features from images. The method replaces the image template in the input of VGG-Net-19 with wavelet coefficients to generate hierarchical convolutional features using five layers. While the last convolutional layers provide robust semantic information of targets and are resistant to appearance variations, their spatial resolution is insufficient for precise localization. However, earlier convolutional layers offer more accurate localization but are vulnerable to appearance changes. The study considers the convolutional layer hierarchies as a nonlinear equivalent of an image pyramid representation and leverages these multiple levels of abstraction for visual tracking. The proposed method learns adaptive correlation filters on each convolutional layer to encode the target appearance and hierarchically infers the maximum response of each layer to locate targets. The study evaluates the approach with specific performance metrics and benchmark datasets, including OTB50, OTB100, TC128, and UAV20, and the results demonstrate that the proposed approach surpasses the performance of state-of-the-art tracking methods, indicating its efficacy in addressing the challenges encountered in visual object tracking.
Retracted: Integrated Development of Artificial Intelligence and Economic Management

[This retracts the article DOI: 10.1155/2022/2970229.].
Performance Assessment in Distributed Teams in the Scope of Software Development: A Study for Management Strategy

This research is part of the context of technological evolution that has enabled software companies to adopt the work model of distributed teams. In this new scenario, the role of management has a great impact considering the absence of a performance assessment model, making it impossible to analyze for decision making, impacting on the assessment of management maturity, productivity and quality of distributed teams. It is advisable that, the positioning before science, is how much the measurement of performance has an impact for a more effective management directed to decision making. Thus, the main objective of the study is to structure a performance assessment model to support the strategic management of the teams distributed within the scope of software development. The structuring of the assessment model was developed through the Multicriteria Methodology of Constructivist Decision Support (MCDA-C), in view of the realization of the construction of knowledge and the confrontation between the sources of knowledge, through data collected through semi-structured interviews with the manager of the distributed teams. The results of the research support a set of information to the context that the performance assessment model was structured, providing the conceptual interpretation of the management planning carried out through strategic, tactical and operational objectives. The conclusions of the study indicate that the performance assessment model proposes the visualization of opportunities for improvement, being possible to identify an overview of indicators that can help management with better decision making in the scenario of software development of distributed teams.
Digital diagnostics: A computer application for lymph node metastases in cervical cancer

BACKGROUND: Since 2020, staging accuracy improvements in cervical cancer (CC) revealed that major errors occur due to missed metastases to regional lymph nodes. The presence or absence of metastases seriously affects the staging of cancer and, consequently, its treatment methods, since metastases make cancer inoperable.&#x0D; AIM: To develop an application to help the doctor in predicting metastases to regional lymph nodes in CC, with the ability of installing on any personal computer regardless of the operating system (Windows, Linux, or Mac OS) and saving data about the patients.&#x0D; METHODS: The development was performed on the .NET platform (framework v4.7.2), by means of C# language, based on a proprietary prognostic model using seven blood parameters such as erythrocyte sedimentation rate, erythrocytes, hemoglobin, fibrinogen, D-dimer, and platelet aggregation with adenosine diphosphate and soluble fibrin monomer complexes.&#x0D; RESULTS: A window application was developed that automatically calculates the probability of metastases to regional lymph nodes in СС and provides the ability to save the data from the input form and the predicted value into TXT and/or CSV files. TXT and CSV formats are supported in various operating systems. The .txt file allows each individual patients prognosis results to be saved in a separate file for easy printing and attachment to the patients electronic or paper medical record. The CSV file format enables the aggregation of data of all patients by adding line-by-line data from the input form to the end of the file.&#x0D; CONCLUSIONS: The developed application facilitates the process of using the patented formula to diagnose metastases to regional lymph nodes in СС at the early stages of examination, which allows choosing the optimal tactics and, consequently, improving the prognosis of the disease.
Designing Assistive Devices to Support Learning for Students with Hearing Impairments

Sign language recognition has become a crucial area of research, especially in the field of assistive technology for hearing- and speech-impaired individuals. This paper presents a new project aimed at developing a device that translates American Sign Language (ASL) into spoken language using the Internet of Things (IoT) and sensors. The system design covers 30 basic gestures and focuses on making communication easier for hearing-impaired students in technical programs. The project addresses the challenges and limitations of sign language recognition, and aims to reduce the communication gap between hearing-impaired and normal individuals. The implementation of this project is expected to result in a more accessible and inclusive society for all.
Gaussian Approximation

In this chapter we again adopt an optimization approach to the problem of Bayesian inference, but instead seek a Gaussian distribution 𝑝 = N(μ, Σ) that minimizes some distance-like measure from the posterior 𝜋𝑦 (u). However, rather than using a metric to define the distance, we use the Kullback–Leibler divergence introduced in Section 4.1.
DXVNet Multimodal Classification Recognition Network Based on Dissymmetric Bidirectional D-GRU

At present, the research based on classification network always has the problem of single data type or fixed model structure, which will affect the processing ability of classification network in the face of practical application. Based on the above problems, this paper designs and implements a new DXVNet multimodal classification and recognition network. First of all, the original traditional two-way GRU structure is reconstructed into a new kind of asymmetric double D - GRU helped structure, was in the past and the future direction of two simple serial output information on building into the output of the information can be used to transfer from the past, have solved the problem of insufficient text characteristic inner link, and can better accumulation of useful information in the past. Secondly, this paper uses Xception pre-training model based on ImageNet and VGG16 pre-training model based on ImageNet to extract two different levels of image features from the same image dataset. Then the image features of two different levels are connected with the text features extracted by D-GRU for multi-modal feature connection. Finally, the fused features are input into the fully connected layer and classified by Softmax, thus forming a new DXVNet multimodal classification network. The experimental results show that the index of DXVNet model is better than other baseline unimodal models and most multimodal models. In the two data sets, the accuracy was improved by about 3.2%–7.63% and 2.94%–7.07% respectively.
Early Breast Cancer Detection based on High-Boost Filtration, Data Fusion and Modified Object Detection

While object detection has made substantial progress in medical image anomaly identification, the deep learning method fails to extract breast cancer images' hidden features. We thus applied traditional image processing techniques, such as high-boost filtration and image subtraction, to extract such hidden information, and generated four processed image datasets. The four types of generated image datasets, together with the original dataset, can be merged via data fusion technology. Our data fusion method first converts all the source images to matrices and then concatenates the matrices. The original dataset, the four types of generated image datasets and the result matrix of data fusion are trained separately with and without attention mechanisms. The data fusion method can increase the mAP by 19.46 percent when combined with the attention mechanisms. The data fusion method that concatenates the source images and embeds the complete information from the source images into the data fusion result may inspire future research in deep learning with matrix concatenation-based data fusion.
FAIR-Enabling Services

(The following is a transcript of my recent podcast episode on this topic.) There is a FAIR Implementation Profile ontology, and it talks about FAIR-enabling resources. So these are corresponding to questions. For each of the fifteen FAIR principles, this FAIR-enabling resource, the idea is that you’ve identified a challenge or you’ve made a choice about some resource that’s going to help you fulfill that – either that resource is available, or it’s planned, or...
Attention Weighted Mixture of Experts with Contrastive Learning for Personalized Ranking in E-commerce

Ranking model plays an essential role in e-commerce search and recommendation. An effective ranking model should give a personalized ranking list for each user according to the user preference. Existing algorithms usually extract a user representation vector from the user behavior sequence, then feed the vector into a feed-forward network (FFN) together with other features for feature interactions, and finally produce a personalized ranking score. Despite tremendous progress in the past, there is still room for improvement. Firstly, the personalized patterns of feature interactions for different users are not explicitly modeled. Secondly, most of existing algorithms have poor personalized ranking results for long-tail users with few historical behaviors due to the data sparsity.To overcome the two challenges, we propose Attention Weighted Mixture of Experts (AW-MoE) with contrastive learning for personalized ranking. Firstly, AW-MoE leverages the MoE framework to capture personalized feature interactions for different users. To model the user preference, the user behavior sequence is simultaneously fed into expert networks and the gate network. Within the gate network, one gate unit and one activation unit are designed to adaptively learn the fine-grained activation vector for experts using an attention mechanism. Secondly, a random masking strategy is applied to the user behavior sequence to simulate long-tail users, and an auxiliary contrastive loss is imposed to the output of the gate network to improve the model generalization for these users. This is validated by a higher performance gain on the long-tail user test set.Experiment results on a JD real production dataset and a public dataset demonstrate the effectiveness of AW-MoE, which significantly outperforms state-of-art methods. Notably, AW-MoE has been successfully deployed in the JD e-commerce search engine, serving the real traffic of hundreds of millions of active users.
Exploiting Reuse for GPU Subgraph Enumeration (Extended Abstract)

Subgraph enumeration is important for many applications such as network motif discovery, community detection, and frequent subgraph mining. To accelerate the execution, recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration. The performances of these parallel schemes are dominated by the set intersection operations which account for up to 95% of the total processing time. (Un)surprisingly, a significant portion (as high as 99%) of these operations is actually redundant, i.e., the same set of vertices is repeatedly encountered and evaluated. Therefore, in this paper, we seek to salvage and recycle the results of such operations to avoid repeated computation. Our solution consists of two phases. In the first phase, we generate a reusable plan that determines the opportunity for reuse. The plan is based on a novel reuse discovery mechanism that can identify available results to prevent redundant computation. In the second phase, the plan is executed to produce the subgraph enumeration results. This processing is based on a newly designed reusable parallel search strategy that can efficiently maintain and retrieve the results of set intersection operations. Our implementation on GPUs shows that our approach can achieve up to 5 times speedups compared with the state-of-the-art GPU solutions.