Reviewerly/exHarmony huggingface dataset:
No Code AI: Automatic Generation of Function Block Diagrams from Documentation and Associated Heuristic for Context-Aware ML Algorithm Training

Industrial process engineering and PLC program development have traditionally favored Function Block Diagram (FBD) programming over classical imperative style programming like the object oriented and functional programming paradigms. The increasing momentum in the adoption and trial of ideas now classified as “No Code” or “Low Code” alongside the mainstream success of statistical learning theory or the so-called machine learning is redefining the way in which we structure programs for the digital machine to execute. A principal focus of “No Code” is deriving executable programs directly from a set of requirement documents or any other documentation that defines consumer or customer expectation. We present a method for generating Function Block Diagram (FBD) programs as either the intermediate or final artifact that can be executed by a target system from a set of requirement documents using a constrained selection algorithm that draws from the top line of an associated recommender system. The results presented demonstrate that this type of No-code generative model is a viable option for industrial process design.
Flow-Based Path Planning for Multiple Homogenous UAVs for Outdoor Formation-Flying

Collision-free path planning is the most crucial component in multi-UAV formation-flying (MFF). We use unlabeled homogenous quadcopters (UAVs) to demonstrate the use of a flow network to create complete (inter-UAV) collision-free paths. This procedure has three main parts: 1) Creating a flow network graph from physical GPS coordinates, 2) Finding a path of minimum cost (least distance) using any graph-based path-finding algorithm, and 3) Implementing the Ford-Fulkerson Method to find the paths with the maximum flow (no collision). Simulations of up to 64 UAVs were conducted for various formations, followed by a practical experiment with 3 quadcopters for testing physical plausibility and feasibility. The results of these tests show the efficacy of this method's ability to produce safe, collision-free paths.
The Eye-Tracking Study of the Film Frame Composition Influence on the Visual Perception

The research is aimed at studying the elements that affect the visual perception of the film frame in order to develop methodological recommendations for the process harmonization of the film frame. The object of research is a film frame. The subject of the research is the technology of film frame processing. The purpose of this work is to obtain experimental data of the film frame viewing pattern and to identify statistical patterns to confirm or refute the formulated hypothesis. The goal of the study is to conduct an experimental study of the influence of composition on the parameters of the film frame viewing pattern. The influence of the factor of the mutual ratio of the areas of the centers of interest to the background on the parameters of the template for viewing the stimulus material is investigated. As a result, the methodology has been developed for conducting experimental studies of human perception of visual information using an eye-tracking software and hardware complex. When analyzing data on the influence of the objects size factor. The results obtained show that under the condition that objects occupy a small area of the frame, the observer needs more time to consider this frame. As well as in the case when objects occupy most of the frame (more than 40%). In the first case, due to the small size of the objects, it becomes more difficult for the observer to find objects in the frame space. In the second case, it takes time to identify objects, since they tend to be perceived as a background due to their large size.
Multi-knowledge Embeddings Enhanced Topic Modeling for Short Texts

Topic models are widely used to extra the latent knowledge of short texts. However, due to data sparsity, traditional topic models based on word co-occurrence patterns have trouble achieving accurate results on short texts. Researchers have recently proposed knowledge-based topic models for short texts to discover more coherent and meaningful topics. Every form of knowledge aims at representing specific and oriented information but is not wide-ranging. Single knowledge-enhanced topic models only take a form of knowledge into count, which is restricted and undesirable. The more forms of knowledge we incorporate, the more comprehensive our understanding of the short text. In this paper, we propose a novel short texts topic model, named MultiKE-DMM, which combines multiple forms of knowledge and the generalized P $$\acute{o}$$ lya urn (GPU) model with Dirichlet Multinomial Mixture (DMM) model. The proposed approach boosts the multi-knowledge background-related words under the same topic. Access to multi-form knowledge permits the creation of an intelligent topic modelling algorithm that considers semantic and fact-oriented relationships between words, offering improved performance over four comparison models on four real-world short text datasets.
Introducing Multi-modality in Persuasive Task Oriented Virtual Sales Agent

In recent years, the usage of virtual assistants to complete tasks like service scheduling and online shopping has increased in both popularity and need. An end user's task goals are the main objectives of a task-oriented conversation agent, and those should be served effectively and successfully. Beside that, user satisfaction is one of the most important aspect that should be taken care of. Communication with multi-modal responses makes the conversation easier and more attractive. Responses through proper images can improve the quality of a task oriented conversation in terms of user satisfaction. Keeping these aspects in mind, we propose a framework which infuses multi-modality with an end-to-end persuasive task oriented dialogue generation module. Additionally, we create a personalised persuasive multi-modal dialogue (PPMD) corpus with slot, sentiment, and agent action annotation at turn level that contains multi-modal responses from both ends. The results and thorough analysis on this dataset show that the suggested multi-modal persuasive virtual assistant achieves better performance over traditional task-oriented frameworks in terms of user satisfaction.
Towards Trustworthy Representation Learning

Representation learning (RL) aims to extract latent features from various types of data and then facilitate a wide range of downstream data analytics tasks, such as classification, clustering, outlier detection, recommender systems, etc. Prior efforts on RL in the past decades mainly focus on developing models to largely retain useful information (e.g., discriminative patterns, semantic knowledge) from data while discard redundant and noisy information. In the data mining and machine learning communities, some recent efforts attempt to encourage both task-oriented performance and trustworthiness of the model, suggesting a pathway towards trustworthy representation learning (TRL). Although trustworthiness has been increasingly discussed from different perspectives (e.g., fairness, explainability, robustness), the intertwined connections between representation learning and trustworthiness have not been formally discussed and clearly revealed yet. In this Blue Sky vision paper, for the first time, we present a conceptual framework that illustrates how to characterize trustworthiness in representation learning, discuss the research challenges, and point out future research opportunities.
CLCDR: Contrastive Learning for Cross-Domain Recommendation to Cold-Start Users

Recent advances in cross-domain recommendation have shown great potential in improving sample efficiency and coping with the challenge of data sparsity via transferring the knowledge from the source domain to the target domain. Previous cross-domain recommendation methods are generally based on extracting information from overlapping users, which limits their performance when there are not sufficient overlapping users. In this paper, we propose a contrastive-based cross-domain recommendation framework for cold-start users that simultaneously transfers knowledge about overlapping users and user-item interactions to optimize the user/item representations. To this end, two contrastive loss functions and two specific learning tasks are proposed. The proposed framework can make fuller use of the information on the source domain and reduce the demand for overlapping users, while maintaining or even enhancing recommendation performance. Experimental results on a real-world dataset demonstrate the efficacy and effectiveness of our framework on the top-N cross-domain recommendation task.
Efficient Policy Generation in Multi-agent Systems via Hypergraph Neural Network

The application of deep reinforcement learning in multi-agent systems introduces extra challenges. In a scenario with numerous agents, one of the most important concerns currently being addressed is how to develop sufficient collaboration between diverse agents. To address this problem, we consider the form of agent interaction based on neighborhood and propose a multi-agent reinforcement learning (MARL) algorithm based on the actor-critic method, which can adaptively construct the hypergraph structure representing the agent interaction and further implement effective information extraction and representation learning through hypergraph convolution networks, leading to effective cooperation. Based on different hypergraph generation methods, we present two variants: Actor Hypergraph Convolutional Critic Network (HGAC) and Actor Attention Hypergraph Critic Network (ATT-HGAC). Experiments with different settings demonstrate the advantages of our approach over other existing methods.
Imbalanced Equilibrium: Emergence of Social Asymmetric Coordinated Behavior in Multi-agent Games

Multi-agent deep reinforcement learning (MADRL) has made remarkable progress but usually requires delicate and fragile reward engineering. Modeling other agents (MOA) is an effective method for compensating for the absence of efficient reward signals. However, existing MOA methods often assume that only one agent can model other non-learning agents. In this study, we propose continuous mutual modeling (CMM), which constantly models other agents that also learn appropriate behaviors from their viewpoints to facilitate the coordination among agents in complex MADRL environments. We then propose a CMM framework referred to as predictor-actor-critic (PAC) in which every agent determines its actions by estimating those of other agents through mutual modeling. We experimentally show that the proposed method enables agents to realize other agents’ activities and promotes the emergence of better-coordinated behaviors in agent society.
FastThaiCaps: A Transformer Based Capsule Network for Hate Speech Detection in Thai Language

The advent of technology has led to people sharing their views openly like never before. Parallelly, cyberbullying and hate speech content have also increased as a side effect that is potentially hazardous to society. While plenty of research is going on to detect online hate speech in English, there is very little research on the Thai language. To investigate how noisy Thai posts can be handled effectively, in this work, we have developed a two-channel deep learning model FastThaiCaps based on BERT and FastText embedding along with a capsule network. The input to one channel is the BERT language model, and that to the other is the pre-trained FastText embedding. Our model has been evaluated on a benchmark Thai dataset categorized into four categories, i.e., peace speech, neutral speech, level-1 hate speech, and level-2 hate speech. Experiments show that FastThaiCaps outperforms state-of-the-art methods by up to 3.11% in terms F1 score.
Building Conversational Diagnosis Systems for Fine-Grained Diseases Using Few Annotated Data

The conversational diagnosis system aims to interview patients and make the diagnosis just like doctors do. Most existing methods rely on medical dialog corpora collected from various medical forums. Compared to experts' annotated data, the dialog corpora are easily accessible, but lack medical knowledge and diagnostic decision logic. Thus, those systems can only handle coarse-grained diseases but achieve poor performance on fine-grained diseases. In this paper, we present a Reinforcement Learning (RL) framework that leverages a few annotated (from experts) and unannotated (from online forums) dialogs to make the diagnosis for fine-grained diseases. We summarize the doctor's diagnosis logic from the unannotated dialogs, then build a user simulator by annotated dialogs for RL training. In this way, a few annotated data are sufficient to support fine-grained disease diagnosis with the assistance of unannotated data. The experiments on eight fine-grained diseases show that our approach outperforms other competitive baselines.
Hierarchical Multimodal Attention Network Based on Semantically Textual Guidance for Video Captioning

Utilizing multiple modal information to understand video semantics is quite natural when humans watch a video and describe its contents with natural language. In this paper, a hierarchical multimodal attention network that promotes the information interactions of visual-textual and visual-visual is proposed for video captioning, which is composed of two attention modules to learn multimodal visual representations in a hierarchical manner. Specifically, visual-textual attention modules are designed for achieving the alignment of the semantic textual guidance and global-local visual representations, thereby leading to a comprehensive understanding of the video-language correspondence. Moreover, the joint modeling of diverse visual representations is learned by the visual-visual attention modules, which can generate compact and powerful video representations to the caption model. Extensive experiments on two public benchmark datasets demonstrate that our approach is pretty competitive with the state-of-the-art methods.
AMRE: An Attention-Based CRNN for Manchu Word Recognition on a Woodblock-Printed Dataset

Ancient minority language character recognition could be challenging due to limited documentation, but it is always critical to better understand history and conduct social science researches. As an important minority language, Manchu language is confronted with the similar challenges due to the lack of systematic document studies. Recently, more researches focus on solving this problem through different approaches, such as document digitalization or character image segmentation. However, there are still some limitations. On one hand, existing digitalized Manchu documents are carried out based upon machine-printed style, which is not common in real historical documents and can cause severe recognition bias. On the other hand, most of Manchu identification methods are based on coarse image segmentation and may result in recognition error since it is difficult to consistently cut the words accurately. To tackle these two challenges, we propose a segmentation-free method for Manchu recognition with a medium scale dataset of Woodblock-printed Manchu Words (WMW). We first develop WMW based-upon woodblock-printed Manchu words, which are more common in ancient documents. With the developed dataset, we conduct document mining and carry out a framework, namely AMRE, with Attention-based Convolutional Recurrent Neural Network. AMRE leverages attention mechanism by weighted aggregation of the convolution results from differently sized kernels and more effectively mine the valid information of morphed words in recognition process. By implementing our proposed AMRE, the digitalized characters can be more accurately recognized. The experiment results show that the word recognition accuracy of AMRE exceeds the baseline by more than 5 $$\%$$ .
Permissioned Blockchain-Based XGBoost for Multi Banks Fraud Detection

Fraud detection is one of the financial institution problems which can utilize Machine Learning (ML). However, the fraud activity is hard to detect since the occurrence is relatively low compared to the actual transaction. Several banks can collaborate to gather more fraudulent transactions from their data. However, the collaboration can cause data leakage from each bank, where the customer data should be confidential. Decentralized ML is one of the approaches to tackle the privacy-preserving aspect. This work proposed a fully decentralized environment using a permissioned blockchain to detect multiple banks' fraud. The training process utilizes a continual eXtreme Gradient Boosting (XGBoost) model. We provided the architecture of blockchain implementation for multiple banks, where it is conducted as batch and streaming data processing. As we compared our approach with the centralized, individual, and federated GBDT models, it maintains a good prediction performance and fulfills the environment of a fully distributed system.
A Hybrid Feature Selection Approach for Data Clustering Based on Ant Colony Optimization

Machine learning, data mining, and pattern recognition all require feature selection when working with high-dimensional data. Feature selection helps in improving the prediction accuracy and significantly reduces the computation time. The problem is that many of the feature selection algorithms use a sequential search strategy to choose the most important features. This means that each time you add or remove a feature from the dataset, you get stuck in a local optimum. This paper proposes a hybrid feature selection technique based on ant colony optimization that randomly selects features and quantifies their quality using K-means clustering in terms of silhouette index and laplacian score. The proposed hybrid feature selection technique allows for random selection of features, which facilitates a better exploration of feature space and avoids the problem of being trapped in a local optimal solution, while also generating a global optimal solution. Furthermore experimental investigation shows that the proposed method outperforms the state-of-the-art method.
Dynamically Connected Graph Representation for Object Detection

Graph models have been applied to object detection task to implement relational reasoning in many works, achieving a tremendous advancement in performance. However, all these models focus on graph representation with shallow layers and the performance decreases with more graph layers. The most crucial problem lies in that deep graph models encounter the vanishing gradient problem, which causes over-smoothing and no diversity among the features of graph nodes. This paper proposes a novel Dynamically Connected Graph (DCG) representation mechanism to break the limitation of the vanishing gradient problem in deep graph-based detection models and make the graph models deeper and better. DCG adaptively learns the feature re-usage and new feature exploration for better optimization of the deep graph. Deep graph models still meet another general problem, i.e., the class imbalance of the original data distribution causes an imbalance in graph node learning. Thus, a Node Balanced Loss is further introduced to address this issue by normalizing and reassigning the weights of losses of various classes. Our method can be easily integrated into existing detection models without extra modification. Experiments prove that DCG significantly improves the performance of various baseline models on the challenging MS-COCO and PascalVOC.
Automatic Identification of Class Level Refactoring Using Abstract Syntax Tree and Embedding Technique

Software refactoring helps improve the quality of the software and increases the execution speed. However, in large and complex systems, it is difficult for software developers to identify the exact code segments that need to be refactored. Many researchers predicted the refactoring model based on source code metrics. The metric value assumption and the tool used to extract metrics values differ from company to company. Our objective is to develop an automated refactoring model based on the code directly. The predictive power of refactoring model depends on the input features computed from the code description. In this work, we have implemented three different word embeddings intending to extract numeric vectors for the refactoring prediction model. Additionally, we have employed three data sampling techniques to solve the data imbalance issue and improve the effectiveness of the proposed model. Furthermore, we have instigated thirteen machine learning classifiers to access the predictive capability of word embedding techniques. Moreover, the Friedman test has been conducted to find the best technique based on rank of all the techniques. The experimental result on four data sets shows that our proposed model with TF-IDF word embedding and BLSMOTE data sampling, RF ensemble learner, improves its predictive capability.
AEGD: adaptive gradient descent with energy

We propose AEGD, a new algorithm for optimization of nonconvex objective functions, based on a dynamically updated 'energy' variable.The method is shown to be unconditionally energy stable, irrespective of the base step size.We prove energy-dependent convergence rates of AEGD for both non-convex and convex objectives, which for a suitably small step size recovers desired convergence rates for the batch gradient descent.We also provide an energy-dependent bound on the stationary convergence of AEGD in the stochastic non-convex setting.The method is straightforward to implement and requires little tuning of hyper-parameters.Experimental results demonstrate that AEGD works well for a large variety of optimization problems.Specifically, it is robust with respect to initial data, capable of making rapid initial progress.The stochastic AEGD shows comparable and often better generalization performance than SGD with momentum for deep neural networks.The code is available at https://github.com/txping/AEGD.
Physics-guided Graph Diffusion Network for Combining Heterogeneous Simulated Data: An Application in Predicting Stream Water Temperature

This paper introduces a new method for combining simulated data over different types of nodes in heterogeneous graphs to facilitate predictive learning. Simulation has been widely used in scientific domains to mitigate the need for a large number of observation samples. However, simulated data are often created separately for each type of physical systems while interactions amongst different types of systems remain unexplored. Our method is developed in the context of predicting water temperature in stream networks, which is critical for decision making in water management. In particular, we first develop a graph diffusion network (GDN) to model the interactions amongst stream segments and reservoirs in a heterogeneous graph. We use the GDN model to combine simulated data for both streams and reservoirs in the graph, and use the obtained composite simulations to train the GDN model in a semi-supervised manner. Then the GDN model is further fine-tuned using true observations. Since observation data are often sparse and localized, we further leverage the information from simulations to build a reweighting strategy so as to migitage the discrepancy between training and testing data. Our evaluations in the Delaware River Basin have shown the superiority of the proposed method over multiple baselines using either sparse or localized training data. The proposed GDN model also creates a better composite simulation dataset for heterogeneous graphs.