Reviewerly/exHarmony huggingface dataset:
KNOWLEDGE-BASED SYSTEMS FOR QUALITY CONTROL AND MONITORING

International competitiveness requires high quality in combination with reduced innovation and throughput time. To improve product quality, quality control loops have to be build. In quality assurance three control loops are to be distinguished: within the machine, close to the machine and machines overlapping control loop. The objective of total quality management must be the integration of these horizontal control loops with vertical exchange of information. Originated by the given situation it becomes clear that high product quality can only be realised by highly developed and controlled processes. For future-oriented quality assurance it is necessary to evaluate process information in short sequences. To react quickly on discovered defects, quality information and data as well as expert knowledge must be available to the whole company. Commercial information systems are often not able to handle this information in a suitable way. Knowledge based systems, especially diagnostic systems, offer a solution for this problem. Online diagnostic systems can be used to build quality control loops close to the machine and machine overlapping.<br><br> This paper describes how to build quality control loops using online diagnostic systems. First the paper gives an insight of the use of knowledge based systems for quality control and monitoring. The differences between integrated control and diagnosis of process parameters are worked out. Finally the implementation of an online diagnostic system using knowledge based methods is introduced.
Federated Learning with Emerging New Class: A Solution Using Isolation-Based Specification

This paper investigates an important classification problem in federated learning (FL), i.e., federated learning with emerging new class or FLENC, where instances with new class that has not been used to train a classifier, may emerge in the testing process. As based on the assumption of the fixed class set, the common FL classification approaches will face the challenge that the predictive accuracy severely degrades if they are used to classify instances with a previously unseen class in FLENC problem. To address this issue, we explore a new FL framework under the one-shot setting, called the federated model with specification. This framework is to learn the local model with the specification which can explain the purpose or specialty of the model, and learn the global federated model in only a single round of communication. Further, we introduce an implementation FLIK by employing a data-dependent isolation-based kernel as the specification. FLIK is the first attempt to propose a unified method to address the following two important aspects of FL: (i) new class detection and (ii) known class classification. We report evaluations demonstrating the effectiveness of our proposed method in FLENC problem.
A KNOWLEDGE-BASED SYSTEM APPROACH TO BENCHMARKING THE TRANSITION TO AGILE MANUFACTURING

The role of artificial intelligence (AI) in manufacturing applications such as agile manufacturing (AM) is a subject of critical importance to efficient and effective information management. The subset of AI with the greatest potential to make a significant contribution to the management of information is knowledge-based systems (KBS); which is a focal point of the research contained in this paper.<br> AM is a rapidly developing methodology of competitive revitalization for the manufacturing sector of the American economy. AM seeks to integrate and synthesize many concepts and theories which have, when implemented in virtual isolation, provided some promise of competitive advantage. A significant dilemma for AM, however, is that few tangible tools have been provided to manufacturers which would manage the flow of information necessary to guide the process of transitioning. The KBS developed for and discussed in this paper, AM-CHECK, will provide that guidance through an innovative integration of two problem-solving methodologies.<br> The two problem-solving methodologies which have been integrated are structured matching and candidate evaluation. Integration of these methodologies has enabled an operational prototype KBS to be created, AM-CHECK which has the ability to process qualitative as well as quantitative data. The implication of a contribution to primary research in AI applications is that an entirely new set of problem-solving algorithms will be available for implementation where there had previously been no tools with the inherent capability.
Disentangling User Intention for Sequential Recommendation with Dual Intention Decoupling Network

Modern recommender systems often use sequential neural networks to capture users' dynamic and evolving intentions from behavior data. However, a user's different intentions might evolve over time at different speeds. Some user intentions are relatively stable with respect to time (i.e., time-invariant), and simply feeding all behavior data to sequential neural networks might not capture these time-invariant intentions well, since the inductive bias of sequential neural networks could prefer time-varying patterns than time-invariant patterns. In this paper, we propose a novel Dual Intention Decoupling Network (DIDN) framework to model time-invariant patterns and time-varying patterns in users' behavior data separately, thus both types of patterns could be modeled more accurately. To do so, we first introduce a self-attention based model and a tree-based clustering algorithm to model time-varying and time-invariant patterns respectively, and then combine these two models to generate the overall click-through rate prediction. In the self-attention module, we further introduce a candidate item attention mechanism to implicitly decouple a user's mixed intentions. Experimental results on three benchmarks show that our DIDN outperforms the state-of-the-art baselines in the topk sequential recommendation task.
Event Relation Extraction Using Type-Guided Attentive Graph Convolutional Networks

Event relation extraction is a fundamental task in text mining, which has wide applications in event-centric natural language processing. However, most of the existing approaches can hardly model complicated contexts since they fail to use dependency-type knowledge in texts to assist in identifying implicit clues to event relations, leading to the sub-optimal performance on this task. To this end, we propose a novel type-guided attentive graph convolutional network for event relation extraction. Specifically, given the input text, the event-specific syntactic dependency graph is first constructed, from which both the local and global dependency knowledge related to events are derived. Then, a dependency-type guided attentive graph convolutional network is designed for learning representations of events, in which the local and global dependency information are utilized to effectively aggregate semantic context among texts. Finally, the event relation is predicted based on the representations of event pair and the representation of the whole text, completing the task of event relation extraction. The experimental results on multiple datasets show that our method significantly outperforms the state-of-the-art baselines. Moreover, further analysis reveals that our method can effectively capture complex relationships between long-distance events, improving accordingly the performance of event relation extraction.
DualFraud: Dual-Target Fraud Detection and Explanation in Supply Chain Finance Across Heterogeneous Graphs

In supply chain finance, detecting fraudulent enterprises and transactions is crucial to minimize financial loss. Enterprises and transactions have heterogeneous information and fraud labels, thus, leveraging such information well can simultaneously improve fraud detection performance in enterprise and transaction domains. This paper describes our newly proposed multitask learning framework, DualFraud, which detects fraudulent enterprises and transactions with explainability based on heterogeneous graphs in supply chain finance. The main contributions of this work are the proposed framework that can facilitate these two domains to share and enhance learning and modelling capabilities to improve fraud projection. The explainer component is attached to generate rich and meaningful explanations for risk controllers across enterprise and transaction graphs. Experiments on datasets prove the effectiveness of fraud detection and explainability in both enterprises and transactions. The proposed DualFraud outperforms the other methods in the selected criteria.
Analysis on Residents’ Satisfaction and Its Influencing Factors with Water Environment Management: Based on the Data from Xiaoqing River

Xiaoqing River is one of the key rivers in the Yellow River Basin, and its management satisfaction is the content that the government should consider when formulating policies.This paper concentrates on residents' satisfaction of water environment management in Jinan section of Xiaoqing River, using questionnaires to find out the problems and effects of Xiaoqing River management.Based on the correlation analysis of the questionnaire data, the results show that five factors including the impact of water pollution, understanding of Xiaoqing River governance, willingness to participate in Xiaoqing River governance, policy publicity, and government regulation have a positive impact on the satisfaction of Xiaoqing River water environment governance.Finally, the paper puts forward some countermeasures and suggestions to increase residents' satisfaction from five aspects, such as increasing publicity efforts, paying attention to the cultivation of public participation consciousness, etc.
Stationary Measures of Three-State Quantum Walks with Defect on the One-Dimension Lattice

In this paper, we focus on the space-inhomogeneous three-state on the onedimension lattice, a one-phase model and a two-phase model include.By using the transfer matrices method by Endo et al., we calculate the stationary measure for initial state concrete eigenvalue.Finally we found the transfer matrices method is more effective for the three-state quantum walks than the method obtained by Kawai et al.
Multi-level Visual Feature Enhancement Method for Visual Question Answering

Visual question answering is a multimodal task that interacts a given image with the corresponding natural language question to get the final answer. Traditional visual question answer models use region-based top-down image feature representations. This approach causes regional features to lose their contextual connection to global features, resulting in the underutilization of the global semantic features of visual features. To solve this problem, it is necessary to enhance the relationships between regions and between regions and the global to obtain more accurate visual feature representations, which can better correlate with corresponding question texts. Therefore, this paper proposes a multi-level visual feature enhancement method (MLVE). It mainly consists of the separated visual feature representation module (SVFR) and the joint visual feature representation module (JVFR). The graph attention neural network is an important part of the two modules to enhance the relationship between regions and between regions and the global. These two modules can learn different levels of visual semantic relationships to provide richer visual feature representations. The effectiveness of this scheme is verified on the VQA2.0 dataset.
THE PHYLOSOPHY OF THE INCLUSIVE EDUCATIONAL SPACE IN MODERN UKRAINE

In the modern world the education is one of the key factors in the development of the society as a social system, and it is also a prerequisite for the effective social and professional fulfillment.This determines the special role of the inclusive education, its ability to adapt the educational space in accordance with the special needs of various social groups.In this instance the inclusive educational space is a process of implementation of special educational programs, services, informational and educational activities, contributing to the sociocultural integration of a person with special needs into the system of social interaction.The experience of integrative programs in Ukraine and around the world in special groups in preschool institutions, schools, high educational institution led to the understanding that, on the one hand, an individual approach that is applied to children with special educational needs is important, and on the other hand, it became obvious that highlighting special classes in schools and cities to children with disabilities often leads to the exclusion of children with disabilities from cultural and social life of the educational institution, creates certain barriers in communication and interaction between the subjects of the educational process.Therefore, from the model of integration they began to move on to the idea of inclusion -joint training and education of children with different opportunities.Successful implementation of inclusive education for person with special educational needs requires changes in the educational system itself.Based on the principles of the social model, society needs to overcome negative attitudes person with special needs and unusual development and give them equal opportunity to fully participate in all spheres of social life.Thus, inclusive education is the next step in the development of education not only for children with special educational needs, but also for the entire educational system.The creation of an inclusive educational space is aimed at solving the problems of social adaptation of students with special educational needs, as well as increasing the level of tolerance of society to this group of people.A developing environment is formed according to the principle of variability, an individual approach is carried out when submitting training material, the child's independent activity is developed, and a modular organization of educational programs is being introduced.All this allows achieving high efficiency of educational activities.
Interpretable Decision Tree Ensemble Learning with Abstract Argumentation for Binary Classification

We marry two powerful ideas: decision tree ensemble for rule induction and abstract argumentation for aggregating inferences from diverse decision trees to produce better predictive performance and intrinsically interpretable than state-of-the-art ensemble models. Our approach called Arguing Tree Ensemble is a self-explainable model that first learns a group of decision trees from a given dataset. It then treats all decision trees as knowledgable agents and let them argue each other for concluding a prediction. Unlike conventional ensemble methods, this proposal offers full transparency to the prediction process. Therefore, AI users are able to interpret and diagnose the prediction’s output.
ALGCN: Accelerated Light Graph Convolution Network for Recommendation

Recently, Graph Convolutional Network (GCN) has been widely applied in the field of collaborative filtering (CF) with tremendous success, since its message-passing mechanism can efficiently aggregate neighborhood information between users and items. However, most of the existing GCN-based CF models suffer from low convergence rates during training, mainly because they follow the design of standard GCN using a simple uniform average to aggregate the neighborhood information. We also find that the scale of embedding across different layers oscillates. We argue that these issues can be alleviated by our proposed graph convolution framework, namely Accelerated Light Graph Convolutional Network (ALGCN). ALGCN mainly contains two components: influence-aware graph convolution operation and augmentation-free in-batch contrastive loss on the unit sphere. Empirical evaluations on three large and public datasets demonstrate that the proposed method achieves remarkable training speedups over LightGCN and substantially outperforms the state-of-the-art GCN-based CF models. Our method also shows a great improvement in long-tail recommendation.
Multi-human Intelligence in Instance-Based Learning

Achieving decision-making that resembles humans is still a challenge for artificial intelligence (AI). Although researchers have successfully used techniques like deep reinforcement learning (DRL) and imitation learning (IL) to develop intelligent behavior in agents, however, such machine-learning-based methods may not resemble human choices. This study addresses this limitation by evaluating how a cognitive model based upon instance-based learning (IBL) theory matches human behavior on a simulation-based search-and-retrieval task. First, the simulation environment was developed using the Unity3D game engine. Next, four human players were recruited to play the simulation to generate human data. This data was then used to initialize the IBL models. In this research, we attempted to improve the quality of human data by sampling portions from the behavior data of multiple humans while maintaining the data size equivalent to the average size of each human’s data. Results revealed that the models driven by the multi-human data doubled in the accuracy of matching the human choices. We also present a novel depiction of how the IBL model’s decision-making improves with the variation in the number of human sources. Techniques where learning from human demonstrations is involved (e.g., IL) may benefit from these results by using multi-human data due to reduced noise and biases.
Sequential Hypergraph Convolution Network for Next Item Recommendation

Graph neural networks have been widely used in personalized recommendation tasks to predict users' next behaviors. Recent research efforts have attempted to use hypergraphs to capture higher-order information among items. However, the existing methods ignore the sequential patterns between different sequences, which can be considered as collaborative signals at the sequential level. Moreover, the multifaceted nature of user behaviors is not sufficiently exploited in existing methods, which may mislead the representation learning of items. In addition, the high computational cost limits the application of hypergraph-based methods. To address these challenges, we propose a novel architecture called the sequential hypergraph convolution network (SHCN) for next item recommendation. First, we design a novel data structure, called a sequential hypergraph, that accurately represents the behavior sequence of each user in each sequential hyperedge. Second, a well-designed node-hyperedge propagation method based on the sequential hypergraph is proposed to effectively capture the sequential patterns between different sequences and utilize the multifaceted nature of user behaviors. Third, we propose a hypergraph sampling strategy to improve the computational efficiency of hypergraph convolution. Extensive experiments on four real-world datasets demonstrate that the proposed method consistently outperforms state-of-the-art methods.
More Efficient and Locally Enhanced Transformer

Aiming at the problems of the expensive computational cost of Self-attention and cascaded Self-attention weakening local feature information in the current ViT model, the ESA (Efficient Self-attention) module for optimizing computational complexity and the LE (Locally Enhanced) module for enhancing local information are proposed. The ESA module sorts the attention intensity of the class token and patch tokens of each Transformer encoder in the ViT model, only retains the weight value of patch token strongly associated with the class token in the attention matrix, and reuses the attention matrix of adjacent layers, so as to reduce the calculation of the model and accelerate the reasoning of the model; the LE module parallels a Depth-wise convolution in each Transformer encoder, it enables Transformer to capture global feature information and strengthen local feature information at the same time, which effectively improves the image recognition rate. A large number of experiments are performed on common image recognition datasets such as Tiny ImageNet, CIFAR-10 and CIFAR-100, experimental results show that the proposed method performs better in recognition accuracy under the premise of less computation.
Leveraging Interactive Paths for Sequential Recommendation

Sequential recommendation systems dynamically predict the users' next behaviors from chronological historical records to provide more accurate recommendations. However, in many real-world scenarios, recommenders find it difficult to make recommendations for cold-start users who have limited interactions with items. To address this issue, existing methods utilize auxiliary information to capture user preferences, which have achieved performance improvement to some extent. But with the gradual awareness of personal privacy, external information becomes difficult to obtain. Therefore, we develop a novel model called Path-based sequential Recommender for cold-start users (PathRec). PathRec exploits the rich sequential dependencies within the interactive paths to give reasonable recommendations for cold-start users without additional external information. Specifically, (i) PathRec constructs the global graph to learn the representations of the items. (ii) Then, PathRec elaborately selects the sampled paths to derive the underlying dependencies of the cold-start sequence. (iii) Finally, PathRec provides rich and complementary information to user-item interactions by strongly coupling the paths to give suitable recommendations. Extensive experiments on three datasets demonstrate the effectiveness of PathRec. In particular, our model can adapt to the cold-start users and has potential interpretability for recommendations.
Bottom-Up Transformer Reasoning Network for Text-Image Retrieval

Image-text retrieval is a complicated and challenging task in the cross-modality area, and lots of experiments have made great progress. Most existing researches process images and text in one pipeline or are highly entangled, which is not practical and human-friendly in the real world. Moreover, the image regions extracted by Faster-RCNN are highly over-sampled in the image pipeline, which causes ambiguities for the extracted visual embeddings. From this point of view, we introduce the Bottom-up Transformer Reasoning Network (BTRN). Our method is built upon the transformer encoders to process the image and text separately. We also embed the tag information generated by Faster-RCNN to strengthen the connection between the two modalities. Recall at K and normalized discounted cumulative gain metric (NDCG) metrics are used to evaluate our model. Through various experiments, we prove our model can reach state-of-the-art results.
FedSpam: Privacy Preserving SMS Spam Prediction

SMSes are a great way to communicate via short text. But, some people take advantage of this service by spamming innocent people. Deep learning models need more SMS data to be adaptive and accurate. But, SMS data being sensitive, should not leave the device premises. Therefore, we have proposed a federated learning approach in our research study. Initially, a distilBERT model having validation accuracy of 98% is transported to mobile clients. Mobile clients train this local model via the SMSes received and send their local model weights to server for aggregation. The process is done iteratively making the model robust and resistant to latest spam techniques. Model prediction analysis is done at server side using global model to check which words in message influence spam and ham. On-device training experiment is conducted on a client and it is observed that the losses of the global model converge after every iteration.