Reviewerly/exHarmony huggingface dataset:
Automatic Key Information Extraction from Visually Rich Documents

Currently, the need for business documents analysis, particularly invoices, is playing a vital role in companies, especially in large ones. These documents have the particularity of being visually rich, with low text quantity and many different layouts. As such, processing them with traditional techniques remains inefficient. Hence, one of the key challenge is to exploit visual patterns between entities of interest. After an overview of the state-of-the-art in this domain, we propose a graph-based model that recognizes specific text in invoices. First, an Encoder module creates a multimodal embedding for each text sequence based on textual, visual, and spatial information. This representation is then passed through a multi-layer graph attention network, before being subjected to a simple classification task. Some experimental results were conducted in order to improve the performance of the proposed approach.
An acceleration method of optical flow calculation for automotive ADAS

An essential component of advanced driver assistance systems (ADAS) is the real-time processing of various image types that are perceived by multiple sources. Although the optical flow is a crucial tool for determining how the camera (or vehicle) is moving in relation to its surroundings, due to the amount of computation involved, it is challenging to use it in real-time. Some of the existing fast optical flow calculation techniques are typically run on a server, which is large, expensive, and consumes a lot of power, making it difficult to mount on a real vehicle. This paper proposes an optimization technique appropriate for embedded GPU s mounted on vehicles that targets the pyramid LK optical flow method. A new dynamic window design strategy based on GPU architecture is proposed. By localizing the feature extraction algorithm to reduce the time-consuming sorting stage, improving the bidirectional constraint evaluation, and ending the iteration as soon as possible, the computational bit width is reduced, improving overall performance. Experiments with a real vehicle scene in the simultaneous localization and mapping (SLAM) system show that our approach meets the real-time requirement with 30 frames per second and improves performance by 3.8 times compared to the GPU-optimized version of OpenCV.
Causal Inference for Personalized Treatment Effect Estimation for given Machine Learning Models

We propose a causal machine learning inference pipeline that combines a given predictive machine learning model with analytical estimations of average treatment effects. It enables to utilize any predictive model for causal inference, which makes it easy to adapt the approach to existing systems. By first estimating the average treatment effect of an intervention on predictors instead of the outcome variable, a causal relationship between an intervention and a wide range of variables is determined. Next, artificial samples are created that are evaluated using the given predictive model to link interventions and outcomes and also allows inferring measurements of uncertainty. Finally, simulations using again the given predictive model are performed to compute measurements of confidence and that allow to compare – according to the given predictive model – the effect of specific treatments. We furthermore demonstrate how this inference engine can be adapted to a privacy-preserving federated learning environment where training data is horizontally distributed across multiple datasets without compromising on our approach’s accuracy. The approach has been evaluated on a use case with a predictive model for the quality of life score of cancer patients, to determine medical interventions to improve their individual quality of life score.
Impact of Work from Home During the Pandemic in Saudi Arabia

The unprecedented health situation in the year 2020 and to some extent 2021 has forced most businesses to operate online with people working from home (WFH). Like almost all countries in the world, Saudi Arabia has suffered from the shocking unstable health situation facing the COVID-19 pandemic. This study investigates different WFH impressions and behaviors that Saudi employees have built during the pandemic and how that has changed over time. We have conducted surveys in two different phases among Saudi employees that have come from varied personal and job-related demographics, including different gender, marital status, cities, managerial roles, job sectors and company sizes. Our data provides a good comprehensive coverage along different demographics. Key findings includes that for 75% of the people it was a brand new experience especially for big companies employees, people's performance and satisfaction depended on the sector that they work for and their marital status, while life work split was the top challenge and flexibility was the top advantage.
Session-based News Recommendation from Temporal User Commenting Dynamics

With the increase in volume of daily online news items, it is more and more difficult for readers to identify news articles relevant to their interests. Thus, effective recommendation systems are critical for an effective user news consumption experience. Existing news recommendation methods usually rely on the news click history to model user interest. However, there are other signals about user behaviors, such as user commenting activity, which have not been used before. We propose a recommendation algorithm that predicts articles a user may be interested in, given her historical sequential commenting behavior on news articles. We show that following this sequential user behavior the news recommendation problem falls into in the class of session-based recommendation. The techniques in this class seek to model users' sequential and temporal behaviors. While we seek to follow the general directions in this space, we face unique challenges specific to news in modeling temporal dynamics, e.g., users' interests shift over time, users comment irregularly on articles, and articles are perishable items with limited lifespans. We propose a recency-regularized neural attentive framework for session-based news recommendation. The proposed method is able to capture the temporal dynamics of both users and news articles, while maintaining interpretability. We design a lag-aware attention and a recency regularization to model the time effect of news articles and comments. We conduct extensive empirical studies on 3 real-world news datasets to demonstrate the effectiveness of our method.
Dynamic Healthcare Embeddings for Improving Patient Care

As hospitals move towards automating and integrating their computing systems, more fine-grained hospital operations data are becoming available. These data include hospital architectural drawings, logs of interactions between patients and healthcare professionals, prescription data, procedures data, and data on patient admission, discharge, and transfers. This has opened up many fascinating avenues for healthcare-related prediction tasks for improving patient care. However, in order to leverage off-the-shelf machine learning software for these tasks, one needs to learn structured representations of entities involved from heterogeneous, dynamic data streams. Here, we propose DECENT, an auto-encoding heterogeneous co-evolving dynamic neural network, for learning heterogeneous dynamic embeddings of patients, doctors, rooms, and medications from diverse data streams. These embeddings capture similarities among doctors, rooms, patients, and medications based on static attributes and dynamic interactions. DECENT enables several applications in healthcare prediction, such as predicting mortality risk and case severity of patients, adverse events (e.g., transfer back into an intensive care unit), and future healthcare-associated infections. The results of using the learned patient embeddings in predictive modeling show that DECENT has a gain of up to 48.1% on the mortality risk prediction task, 12.6% on the case severity prediction task, 6.4% on the medical intensive care unit transfer task, and 3.8% on the Clostridioides difficile (C.diff) Infection (CDI) prediction task over the state-of-the-art baselines. In addition, case studies on the learned doctor, medication, and room embeddings show that our approach learns meaningful and interpretable embeddings.
DMNP: A Deep Learning Approach for Missing Node Prediction in Partially Observed Graphs

Missing data is unavoidable in graphs, which can significantly affect the accuracy of downstream tasks. Many methods have been proposed to mitigate missing data in partially observed graphs. Most of these approaches assume they have complete access to graph nodes and only focus on recovering missing links, while in practice a part of the graph nodes can also be out of access. This work presents Deep Missing Node Predictor (DMNP), a novel deep learning-based approach to recovering missing nodes in partly observed graphs. Our proposed approach does not rely on additional information that in many cases does not exist. We compare our model with graph completion and deep graph generation baselines. The experimental results show that the DMNP model outperforms previous state-of-the-art approaches.
Text Mining with Information Extraction for Chinese Financial Knowledge Graph

Financial Documents reveal important financial information about a company's financial performance which plays a vital role not only to the stakeholders but also to the public. Therefore, many researchers utilize dynamic Text mining methods in financial document to identify, analyze, predict or evaluate a company's future financial value. In order to find deeply the relationship between companies and the stakeholders, provide a simplified method for them to identify the future financial performance of the corporation. In this paper, we present a Chinese Information Extraction System (CFIES) for Financial Knowledge Graph (FinKG). The major findings of the research show an increased importance of the key audit matters in finance. The major research contribution of this paper is that we have developed CFIES which can extract the tuples from the financial reports. The adoption of the information system can assist the development of a knowledge graph that can discover deep financial knowledge in the finance domain. The managerial implication is that building CFIES can efficiently enable us to clarify the complicated relationship between the corporations, board of directors, investors, and especially the asset, assisting the stakeholders to discover a new financial knowledge representation and to make a financial decision.
Comparison between Inductive and Transductive Learning in a Real Citation Network using Graph Neural Networks

Graph data is present everywhere and has vast ranging applications from finding the common interests of people to the optimization of road traffic. Due to the interconnectedness of nodes in graphs, training neural networks on graphs can be done in two settings: in transductive learning, the model can have access to the test features in the training phase; in the inductive setting, the test data remains unseen. We explore the differences between inductive and transductive learning on real citation networks when the graphs are converted to undirected graphs. We find that the models achieve better accuracy in the transductive setting than in the inductive setting, but that the gap between validation and test accuracy is also higher, which indicates the models trained in an inductive setting have better generalization capabilities.
Universal Graph Embedding Fine Tuning with Dirichlet Energy Smoothing

Traditionally, networks are interpreted as discrete entities with nodes connected by links. In this work we propose to interpret networks as fields describing the distribution of certain properties in the multidimensional space. By following the field interpretation of networks, we introduce a universal fine-tuning of node embed dings using the concept of Dirichlet energy smoothing to obtain desirable properties of node embeddings.
Adapt to Non-stationary Environments via Multi-teacher and Single-Student Process

A key challenge in reinforcement learning (RL) is that a policy trained in simulation fails to solve the same task when applied to the real world that is non-stationary or noisy. A common practice is to account for the environmental information that may change in the real world and leverage RL algorithms to train a generalized policy. However, this policy is overly conservative since it does not make sufficient use of environmental information. In this paper, we present a three-stage method for training a policy that can adapt to non-stationary environments. The first stage is to train multiple teachers under various numerical ranges of environmental information. The second stage is to distill multiple teachers into one single student policy. Finally, we train an adaptation module that can infer environmental information from historical data using a recursive model. We experimentally show that our method outperforms commonly used policy generalization methods in a range of environments.
A Novel Single-Axis Solar Tracker Based on Reinforcement Learning

Solar panels with fixed angles can hardly consistently achieve maximum power output because the sun’s rays are not always perpendicular to the panel. To address this problem, a single-axis solar tracker based on reinforcement learning (RL) is designed in this paper. The proposed RLbased solar tracker has a simple structure that no additional sensors or positioning devices are required. Therefore, the maintenance of the proposed tracker is easy to perform. Simulations have been conducted to validate the effectiveness of the proposed solar tracker. The results demonstrate that the mean absolute error (MAE) between the tracking tilt angle and the theoretical value is within a one-eighth degree.
Reliability Evaluation for IoT-Based Monitoring System Based on Cloud Models and Probabilistic Linguistic Terms Sets

The Internet of things (IoT) technology is becoming more and more sophisticated in all aspects of life and the involvement of IoT will be seen in all aspects of the development of the internet of things system has brought great convenience and different technical specifications for automation monitoring. As the number of devices connected to the IoT system is increasing, its expansion has become a major design goal in its automated IoT monitoring system. It can greatly solve the shortcomings of the traditional monitoring software to achieve all-round monitoring, so that people can enjoy a comfortable network environment for life and work. The reliability problem of IoT monitoring system appears along with it, so this paper focuses on the reliability evaluation for IoT monitoring system, and the cloud model assessment method is used to assess the IoT monitoring system. And the factors within the IoT monitoring system that are most influential to the reliability are identified by the probabilistic linguistic terms sets.
Common Spikes Success Factors: An Industrial Investigation within Agile Software Development

Using spikes in agile software development (ASD) can help organisations produce high-quality software by utilising the necessary technical expertise, optimising the entire development process, and ensuring the client's requirements are met. The purpose of this study is to identify the common spikes success factors (CSSFs) that aid in effective application and recognise glitches that may be caused by a lack of knowledge, which will serve in investigating the uncertainty and risks connected with software projects. Establishing CSSFs is critical for ensuring best practices and identifying the items influencing a spike's outcomes. The factors in this study were developed via interviews and surveys of experts in the software industry. Experts and practitioners had a significant role in determining what spikes need to achieve in software development and what aids in their completion. The study findings indicated that CSSFs could influence the effective application of spikes, which in turn affects the technical and functional aspects of the software being developed.
An Improved XGBoost-Based Model for Detecting Abnormal Transaction Behavior of Online Shopping Consumers

In view of the abnormal behavior of online shopping consumers, this paper integrates machine learning algorithms, takes online shopping consumers' purchase data as the research object, mines effective information, uses PCA algorithm to extract features by dimensionality reduction for high latitude and strong correlation in click data, and uses One Class SVM algorithm to solve the problem of imbalance between normal and abnormal transaction behaviors. A classification of consumption behavior based on XGBoost algorithm in the integration algorithm is proposed to improve the anti-noise ability as well as generalization ability of machine learning algorithm through the combination of algorithms. The model is verified by K-fold cross-validation algorithm, and the performance of abnormal behavior detection model of online shopping consumers is described by F1-score, which verifies the effectiveness of this method in abnormal behavior detection.
Research Progress of Deep Learning in Stomatology Diagnosis

With the development of computer science and technology, artificial intelligence is becoming mature in every field of life, and the medical The combination of deep convolutional neural networks with various fields in the medical industry is also an emerging The combination of deep convolutional neural networks with various fields in the medical industry is also an emerging direction of medical research, which has made impressive achievements in diagnosis and prediction in medical imaging and pathology. The medical industry is stepping into the era of artificial intelligence. deep learning developed from artificial intelligence is changing the traditional Deep learning developed from artificial intelligence is changing the traditional medical diagnosis and treatment model and is bound to be an important development trend of future medicine. This paper reviews the development history of deep learning and the research development of deep learning in assisted diagnosis of stomatology.
Research on Artificial Intelligence-Related Technologies for Internet of Things Applications

With the rapid development of science and technology, artificial intelligence technology has been pushed to a new height of technical research.lt not only greatly promoted the reform of productive forces, but also effectively drove the development of market economy.Today’s era is the era of artificial intelligence, and the Internet of Things technology undoubtedly plays a crucial role in the integration of artificial intelligence into daily production and life.Through the Internet of things technology, artificial intelligence and communication technology are combined to form a huge intelligent system between things, so as to realize the intelligent management of production and life with the network.This article will explore the application and development prospects under the Internet of Things.Intelligent technology has great value in the production and development of various industries.The application of artificial intelligence technology has effectively promoted the development of the Internet of Things technology. This paper will make a simple analysis of the artificial intelligence related technologies applied in the Internet of Things for discussion by industry insiders.
On Developing a Social Message Classification Platform for User-Specified Topics

The information people receive from social media contains a variety of topics, and these massive amounts of information are often disorganized. It is difficult for people to get valuable information directly from it. The advantage of our approach is two-fold: to accommodate users of various interests, and Human-in-the-Loop can be responsible for guiding AI system learning. And machine learning is one of the standard methods that is widely used for data classification tasks. The development of machine learning processes often requires efforts of data labeling and programming, which is often time-consuming and labor-intensive. To help non-experts better understand the model training process, we design a no-code data classification platform. It uses messages crawled from social media as data sources, filters data, and incorporates active learning methods to reduce the cost of creating datasets. Our platform uses an automated process to help users complete model training through simple web operations without programming. The ultimate goal is that users can intervene in the process of model training, quickly perform classification tasks and obtain information according to their needs.
Extracting Knowledge from and for Meetings

Meetings are a fundamental part of a group decision-making process. As organizations grow across the globe and remote teams rise, the meetings become even more complex. Therefore, new technologies expanded the definition of a meeting as a face-to-face situation to an activity distributed in space and time. In this research, we identify similarities among meetings types and classify them using knowledge management principles. This information may be used to help with planning meetings, capturing information, reusing knowledge, and creating best practices tailored for each meeting type. We also develop a meeting transcription application, which interfaces with transcription software to extract information from meetings and index them for further use. This transcription strategy facilitates communication, bringing flexibility and efficiency by automatically transcribing meetings in a text while providing a more accurate and reliable method of meeting documentation. A use case is described using the solution, to exemplify its operation.
A Study on the Bayesian Network Approach with K-Anonymity in Contact Tracing Applications for COVID-19

Due to the rise of severe and acute infections called Coronavirus 19, contact tracing has become a critical subject in medical science. A system for automatically detecting diseases aids medical professionals in disease diagnosis to lessen the death rate of patients. To automatically diagnose COVID-19 from contact tracing, this research seeks to offer a deep learning technique based on integrating a Bayesian Network and K-anonymity. In this system, data classification is done using the Bayesian Network Model. For privacy concerns, the K-anonymity algorithm is utilized to prevent malicious users from accessing patients' personal information. The dataset for this system consisted of 114 patients. The researchers proposed methods such as the K-anonymity model to remove personal information. The age group and occupations were replaced with more extensive categories such as age range and numbers of employed and unemployed. Further, the accuracy score for the Bayesian Network with k-anonymity is 97.058%, which is an exceptional accuracy score. On the other hand, the Bayesian Network without k-anonymity has an accuracy score of 97.1429%. These two have a minimal percent difference, indicating that they are both excellent and accurate models. The system produced the desired results on the currently available dataset. The researchers can experiment with other approaches to address the problem statements in the future by utilizing other algorithms besides the Bayesian one, observing how they perform on the dataset, and testing the algorithm with undersampled data to evaluate how it performs. In addition, researchers should also gather more information from various sources to improve the sample size distribution and make the model sufficiently fair to generate accurate predictions.