Reviewerly/exHarmony huggingface dataset:
KGESS - A Knowledge Graph Embedding Method Based on Semantics and Structure

To achieve a better performance in the downstream task of knowledge graph (KG), a good representation of KG is necessary. Sensing from the topological structure of the graph, most conventional methods tend to ignore the semantic features of nodes, which is significant for describing the entity in KG. In this paper, we propose a novel Knowledge Graph Embedding method based on Semantics and Structure (KGESS), which learned the representation of KG from both topological facts and semantic information. It leverages Chinese BERT to obtain semantic features of the entity first. Then it further enhances these features via a neural module, namely Semantic Feature Extractor. To evaluate the performance of KGESS, we utilize an additional linear module to execute the link prediction task. Experimental results demonstrate that KGESS achieves a superior Hit@k score than conventional methods, indicating the effectiveness of the idea of enhancing structure with semantics in the representation task of KG.
MGR: Metric Learning with Graph Neural Networks for Multi-behavior Recommendation

Traditional recommendation methods often suffer from the problems of sparsity and cold start. Therefore, researchers usually leverage Knowledge Graph as a kind of side information to alleviate these issues and improve the accuracy of recommendation results. However, most existing studies focus on modeling the single behavior of user-item interactions, ignoring the active effects of the multi-type behavior information in the recommendation performance. In view of this, we propose Metric Learning with Graph Neural Networks for Multi-behavior Recommendation (MGR), a novel sequential recommendation framework that considers both temporal dynamics and semantic information. Specifically, the temporal encoding strategy is used to model dynamic user preferences. In addition, the Graph Neural Network is utilized to capture the information from high-order nodes so as to mine the semantic description in multi-behavior interactions. Finally, symmetric metric learning helps to sort the item list to accomplish the Top-K recommendation task. Extensive experiments in three real-world datasets demonstrate that MGR outperforms the state-of-the-art recommendation methods.
Letter Recognition in Hand Sign Language with VGG-16

Instead of spoken language, sign language is the commonly used language by the deaf. To express their thoughts, the deaf people combine handshapes, movements, directions of hands, arms and body, and facial expressions. Currently, there are several methods of hand sign language recognition, such as image recognition or sensor recognition. In this study, we tested the recognition of characters of hand sign language using VGG-16 network architecture to investigate the change in recognition efficiency when adding Dense layers with neural parameters. A test was conducted on a gesture dataset consisting of 26 English letters with 5,391 images, and there are 200 images for each subclass. By investigating the change in recognition efficiency through the change of the VGG-16 model, we have achieved a high recognition rate with an accuracy of 99.902% on the training set and 99.910% on the test set compared to the original model. In addition, the change in the distance when detecting hand gestures also affects the system’s accuracy.
Multi-task and Generative Adversarial Learning for Robust and Sustainable Text Classification

Modern neural networks are quite demanding regarding the size and coverage of adequate training evidences, as far as complex inferences are involved. This is the case of offensive language detection that focuses on a phenomenon, the recognition of offensive uses of language, that is elusive and multifaceted. In this scenarios gathering training data can be prohibitively expensive and the dynamics and multidimensional nature of the abusive language phenomena are also demanding of timely and evolving evidence for training in a continuous fashion. The MT-GAN-BERT approach proposed here aims to reduce the requirements of neural approaches both in terms of the amount of annotated data and the computational cost required at classification time. It focuses corresponds to a general BERT-based architecture for multi faceted text classification tasks. On the one side, MT-GAN-BERT enables semi-supervised learning for Transformers based on the Generative Adversarial Learning paradigm. It also implements a Multi-task Learning approach able to train over and solve multiple tasks, simultaneously. A single BERT-based model is used to encode the input examples, while multiple linear layers are used to implement the classification steps, with a significant reduction of the computational costs. In the experimental evaluations we studied six classification tasks related to the detection of abusive uses of language in Italian. Outcomes suggest that MT-GAN-BERT is sustainable and generally improves the raw adoption of multiple BERT-based models, with much lighter requirements in terms of annotated data and computational costs.
Multi-Attention Relation Network for Figure Question Answering

Figure question answering (FQA) is proposed as a new multimodal task for visual question answering (VQA). Given a scientific-style figure and a related question, the machine needs to answer the question based on reasoning. The Relation Network (RN) is the proposed approach for the baseline of FQA, which computes a representation of relations between objects within images to get the answer result. We improve the RN model by using a variety of attention mechanism methods. Here, we propose a novel algorithm called Multi-attention Relation Network (MARN), which consists of a CBAM module, an LSTM module, and an attention relation module. The CBAM module first performs an attention mechanism during the feature extraction of the image to make the feature map more effective. Then in the attention relation module, each object pair contributes differently to reasoning. The experiments show that MARN greatly outperforms the RN model and other state-of-the-art methods on the FigureQA and DVQA datasets.
Data-Driven Approach for Investigation of Irradiation Hardening Behavior of RAFM Steel

Knowledge reasoning plays an important role in applications such as the human relation web and semantic search. However, how to use this method to solve materials problems is still a challenge. Defects and damage induced by neutron irradiation significantly affect the service performance of materials. Reduced Activation Ferritic/Martensitic (RAFM) steel is a very promising candidate for application in fusion reactor cladding. Understanding irradiation hardening effects in RAFM steel is one of the critical issues. Some experimental data of RAFM steel under irradiation are collected to construct a data set. The relationship between yield strength variation after irradiation and elements and irradiation conditions is trained by the machine learning method. The influence of irradiation condition and alloy elements on the hardening behavior of RAFM steel was explored, and some optimal alloy elements composition was also recommended. This work will give some direction for RAFM steel research.
Methods for Subheading Recognition in Recruitment Information

In the Internet age, talent needs in various industries are changing rapidly, pushing job seekers to learn the latest practical skills to adapt to this change. We can mine some important information from online recruitment information to help job seekers improve their competitiveness, but the premise is to segment the whole recruitment information into fragments of different topics for different analytical objectives. Regarding the description of job responsibilities, it is valuable to distill the most basic and core missions which is common in all workplaces. And different functional parts have different mining values. In this paper, three efficient artificial intelligent models including Adaptive Boosting (AdaBoost), Support Vector Machine with Bootstrap Aggregating (SVM+Bagging) and Fasttext are proposed to recognize subheadings in the recruitment information without the help of font styles and HTML tagsaiming to implement the segmentation by subheadings. Fasttext is modified to consider the effect of the recruitment information&#x0027;s other parts on whether a line becomes or contains a subheading. According to the experimental results, the three methods have a good performance on the test set, showing a respectable generalization ability. AdaBoost (precision: 0.9770 &#x003C; recall: 0.9853) and SVM+Bagging (precision: 0.9874 &#x003E; recall: 0.9714) put different emphasis on subheading recognition, while the modified Fasttext has the best comprehensive performance (F1 value: 0.9849) and the highest precision (0.9894). This study makes a preliminary contribution to topic classification and valuable information extraction from recruitment information fragments.
KIR: A Knowledge-Enhanced Interpretable Recommendation Method

Recommendation System (RS) is of great significance for screening adequate information and improving the efficiency of information acquisition. The existing recommendation methods can improve the accuracy of the recommendation results to a certain extent. However, due to the lack of interpretability, the recommendation results are insufficient and burdensome to satisfy the desire of some users to understand the recommendation basis. For this reason, the Knowledge Graph (KG) is introduced as auxiliary information of the RS. It calculates the importance of entities and relations and uses them as the recommendation’s basis. First, item categories are introduced to enhance the impact on user preferences. Second, an Attention Mechanism (AM) is proposed to distinguish users’ interests in different contextual entity-relation sets in the KG. Finally, multiple feature information is input into the Convolutional Neural Network (CNN) to extract users’ preferences. The experimental results prove that the model can improve the accuracy of the recommendation effect, and at the same time, it can better explain the reasons for the recommendation.
Generating Local Textual Explanations for CNNs: A Semantic Approach Based on Knowledge Graphs

Explainable Artificial Intelligence (XAI) has recently become an active research field due to the need for transparency and accountability when deploying AI models for high-stake decision making. In Computer Vision, despite state-of-the-art Convolutional Neural Networks (CNNs) have achieved great performance, understanding their decision processes, especially when a mistake occur, is still a known challenge. Current XAI methods for explaining CNNs mostly rely on visually highlighting parts of the image that contributed the most to the outcome. Although helpful, such visual clues do not provide a deeper understanding of the neural representation and need to be interpreted by humans. This limits scalability and possibly adds bias to the explainability process, in particular when the outcome is not the one expected. In this paper, we propose a method that provides textual explanations for CNNs in image classification tasks. The explanations generated by our approach can be easily understood by humans, which makes our method more scalable and less dependent on human interpretation. In addition, our approach gives the opportunity to link neural representations with knowledge. In the proposed approach we extend our notion of co-activation graph to include input data and we use such graph to connect neural representations from trained CNNs with external knowledge. Then, we use link prediction algorithms to predict semantic attributes of unseen input data. Finally, we use the results of these predictions to generate factual and counterfactual textual explanations of classification mistakes. Preliminary results show that when the link prediction accuracy is high, our method can generate good textual factual and counterfactual explanations that do not need human interpretation. Despite a more extensive evaluation is still ongoing, this indicates the potential of our approach in combining neural representations and knowledge graphs to generate explanations for mistakes in semantic terms.
Building Narrative Structures from Knowledge Graphs

Humans constantly create narratives to provide explanations for how and why something happens. Designing systems able to build such narratives would therefore contribute to building more human-centric systems, and to support uses like decision-making processes. Here, a narrative is seen as a sequence of events. My thesis investigates how a narrative can be built computationally. Four research questions are identified: representation, construction, link prediction and evaluation. A case study on the French Revolution, based upon Wikidata and Wikipedia is presented. This prototype helps identifying the first challenges such as dynamic representation and evaluation of a narrative.
An Interpretability Algorithm of Neural Network Based on Neural Support Decision Tree

In view of the poor interpretability of the current neural network models, the neural support decision tree model is used to enhance its interpretability. The model combines the characteristics of high recognition accuracy of neural network and strong interpretation of decision tree. We employ the ResNet18 model to solve the gradient disappearance problem with the increase of network depth. By constructing induction hierarchy and establishing hierarchy in weight space, a higher accuracy is obtained. The hierarchical structure derived from the model parameters is adopted to avoid over fitting. And the trained network weights are utilized to construct a tree structure to complete the tree monitoring loss training, and the classification network is retrained or finetuned with additional hierarchy-based loss items. We exploit the neural network backbone to characterize each sample, and establish a decision tree in the weight space is run to enhance the interpretability of the model. At the same time, the optimization of the model is completed. Compared with the original model, the traditional hard decision tree reasoning rules are abandoned and the soft decision tree reasoning rules are adopted to complete the soft tree supervision loss to improve the classification accuracy and generalization ability of the model, which not only ensures high accuracy, but also completes the explicit display of recognition and classification process.
DISEL: A Language for Specifying DIS-Based Ontologies

We present the main syntactical constructs of DISEL language, which is designed as a language for specifying DIS-based ontologies. The adoption of this language would enable the creation of shareable ontologies for the development of ontology-based systems. We give the main constructs of the language and we illustrate the specification of the main components of a DISEL ontology using a simplified example of a weather ontology. DIS formalism, on which the proposed language is based, enables the modelling of an ontology in a bottom-up approach. The syntax of DISEL language is based on XML, which eases the translation of its ontologies to other ontology languages. We also introduce DISEL Editor tool, which has several capabilities such as editing and visualising ontologies. It can guide the specifier in providing the essential elements of the ontology, then it automatically produces the full DISEL ontology specification.
Classification of Heads in Multi-head Attention Mechanisms

Transformer model has become the dominant modeling paradigm in deep learning, of which multi-head attention is a critical component. While increasing Transformer effect, it also has some issues. When the number of heads reaches a point, some attention heads have remarkably similar attention graphs, which indicates that these heads are doing repetitive calculations. Some heads may even focus on extraneous things, affecting the final result. After analyzing the multi-head attention mechanism, this paper believes that the consistency of the inputs to the multi-head attention mechanism is the underlying reason for the similarity of the attention graph between heads. For this reason, this paper proposes the concept of classifying the heads in multi-head attention mechanism and summarizes the general classification process. Three classification schemes are designed for the Multi30k dataset. Experiments demonstrate that our method converges faster than the baseline model and that the BLEU improves by 3.08–4.38 compared to the baseline model.
A Study on the Psychology of Game Design Based on the Hierarchical Theory of Needs

In order to study the influence of Maslow's hierarchy of needs on the psychology of game design, a research analysis using literature analysis and observation is conducted in this paper to discuss the needs of the bottom motivation, preliminary needs, basic game needs, emotional extrapolation needs and high-level gameplay needs in game design.Through the analysis of Maslow's relevant psychological theories and linking these valuable psychological theories to the current situation of game design, the different influences of Maslow's hierarchy of needs on game design are explored and further discussed at the lower and higher levels of needs respectively, and the importance of Maslow's hierarchy of needs on game design is derived.On this basis, the design strategies that should be adopted to satisfy players' needs in game design are proposed.
Integrating Quaternion Graph Convolutional Networks with Tucker Decomposition for Link Prediction on Knowledge Graphs

A knowledge graph is a knowledge base that provides a structured representation of real-world entities, i.e., objects, events, or concepts. Interactions between these entities are illustrated through relationships. However, we can not integrate all human knowledge into graphs. Besides, the real world is dynamic and ever-evolving, so they typically suffer from incompleteness. The task of link prediction is to solve this challenge by inferring missing associations between entities based on existing ones. In this paper, we introduce a new model named QTuckER, which utilizes a pointwise mutual information (PMI) word-word matrix as an adjacency matrix for Graph Convolutional Networks via quaternion multiplication. Specifically, an adjacent matrix is created based on the co-occurrence probability of entities and relations divided by the probabilities of them appearing individually. Then, Quaternion Graph Convolutional Networks are given as an encoder module to update entity and relation vector representations from a row-normalized PMI matrix. Finally, the TuckER model is used as a decoder module to calculate the score for each triple in the graph. Experimental results show that QTuckER obtains better results than previous translation-based and tensor decomposition-based models on two standard benchmark datasets.
Scientific Item Recommendation Using a Citation Network

Scientific items (such as papers or datasets) discovery and reuse is crucial to support and improve scientific research. However, the process is often tortuous, and researchers end up using less than ideal datasets. Search engines tailored to this task are useful, but current systems only support keyword searches. This hinders the process, because the user needs to imagine what kind of keywords would give useful datasets. In this paper, we investigate a new technique to recommend scientific items (paper or datasets). This technique uses a graph consisting of scientific papers, the corresponding citation network, and datasets used in these works as background information for its recommendation. Specifically, a link-predictor is trained which is then used to infer useful datasets for the paper the researcher is working on. As an input, it uses the co-author information, citation information, and the already used datasets. To compare different scientific items recommendation approaches fairly and to prove their efficiency, we created a new benchmark. This benchmark includes more than three million scientific items to evaluate the performance of recommendation approaches. We experiment with a variety of methods and find that an ensemble technique which uses link prediction on the citation network yields a precision of nearly 70%.
Streaming Property

People acquire property rights in objects and real estate in order to capture the stream of services that these assets can provide over time. The thing or parcel itself is merely a delivery mechanism, a way of packaging and protecting rights to that value stream. And, significantly, these assets cannot stream services to anyone without a set of facilitating conditions and complementary goods, such as public infrastructure, that do not lie within the asset owner’s individual control. This Essay argues that we can gain fresh traction on inequality by recasting property as service streams rather than as owned things. Doing so emphasizes the costs of structuring property entitlements in ways that monopolize or squander streams of services. It also reveals new opportunities to repackage valuable service streams into asset-like formats to create durable and flexible claims on resources. Finally, a focus on services directs fresh attention to what people actually want and need from resources over time—a dynamic inquiry whose answer encompasses the spatial and ecological services that are now of overwhelming importance.
Modular Logic Argumentation in Arg-tuProlog

A modular extension of Arg-tuProlog, a light-weight argumentation tool, is here presented and discussed, highlighting how it enables reasoning with rules and interpretations of multiple legal systems. Its effectiveness is demonstrated with examples from different national private international law (PIL) laws, running in Arg-tuProlog. PIL addresses overlaps and conflicts between legal systems by distributing cases between the authorities of such systems (jurisdiction) and establishing what rules these authorities have to apply to each case (choice of law).
Occlusion Robust Face Recognition Based on Mask Learning with Attention Mechanism

Wearing masks in public places is an efficient strategy to prevent infection and slow the spread of COVID-19. However, masked face recognition is challenging due to a lack of information about facial features in the masked area. We present a unique Switching Replacement Attention Network (SRAN) for robust face identification based on attention mechanism, which is inspired by the human visual system, which exclusively focuses on non-occluded facial areas. Firstly, a replacement module is established by training a segmentation network to segment the location of the occlusion item. To exclude the corrupted feature components from recognition, we multiply the occluded object’s segmentation mask with the original image features. To determine when the replacement module is applied, we use a lightweight switch module that is both fast and accurate. The proposed technique outperforms state-of-the-art systems on a variety of occluded and non-occluded facial recognition datasets, according to test results.
ArabCeleb: Speaker Recognition in Arabic

Due to the growing interest in speech recognition technologies, several datasets of speech acquired under uncontrolled conditions have been proposed in recent years. The majority of the datasets available to the community are in English, which reduces the possibility of developing and evaluating recognition technologies in languages other than English. In this paper we try to reduce this language-related gap by proposing a dataset for Arabic language speech recognition. The dataset is made available to the community and contains 100 speakers of both genders. Experiments with some of the latest speaker recognition approaches have been performed both with and without a suitable training on the Arabic language. Results suggest that, to effectively develop recognition technologies in other languages, suitable data for that language are necessary to allow at least a transfer learning approach. In particular, such data is crucial when short utterances are considered.