Reviewerly/exHarmony huggingface dataset:
Continual Learning for Table Detection in Document Images

The growing amount of data demands methods that can gradually learn from new samples. However, it is not trivial to continually train a network. Retraining a network with new data usually results in a phenomenon called “catastrophic forgetting”. In a nutshell, the performance of the model on the previous data drops by learning from the new instances. This paper explores this issue in the table detection problem. While there are multiple datasets and sophisticated methods for table detection, the utilization of continual learning techniques in this domain has not been studied. We employed an effective technique called experience replay and performed extensive experiments on several datasets to investigate the effects of catastrophic forgetting. The results show that our proposed approach mitigates the performance drop by 15 percent. To the best of our knowledge, this is the first time that continual learning techniques have been adopted for table detection, and we hope this stands as a baseline for future research.
High optical contrast nanoimprinted speckle patterns for digital image correlation analysis

For the characterization of the mechanical deformation of materials at microscopic length scales, image processing of a high-quality surface pattern was used. We imprinted speckle patterns onto a thin polymer film attached to the surface of flat and curved metal substrates using flexible molds and soft-thermal nanoimprint lithography. High optical contrast was achieved by mixing black dye into the film generating high absorption in the elevated structures, and by adding titania nanoparticles as fillers to the recessed areas to induce diffuse scattering. For accessing resolution suitable to detect deformation at an individual grain level, the structure sizes were scaled down from 20 μm to 2 μm. For both structure sizes imaging was tested using a digital image correlation setup, that enables 3D imaging of samples with angles of up to 10° of inclination.
Prediction of photovoltaic power output based on similar day analysis using RBF neural network with adaptive black widow optimization algorithm and K-means clustering

Solar photovoltaic power generation has become the focus of the world energy market. However, weak continuity and variability of solar power data severely increase grid operating pressure. Therefore, it is necessary to propose a new refined and targeted forecasting method to broaden the forecasting channels. In this paper, a hybrid model (KM-SDA-ABWO-RBF) based on radial basis function neural networks (RBFNNs), adaptive black widow optimization algorithm (ABWO), similar day analysis (SDA) and K-means clustering (KM) has been developed. The ABWO algorithm develops adaptive factors to optimize the parameters of RBFNNs and avoid getting trapped in local optima. SDA and K-means clustering determine the similarity days and the optimal similarity day through meteorological factors and historical datasets. Nine models compared forecast accuracy and stability over four seasons. Experiments show that compared with other well-known models on the four indicators, the proposed KM-SDA-ABWO-RBF model has the highest prediction accuracy and is more stable.
Visualization of Digital Transformation of Industrial Production into the Educational Process

This paper considers the technology of creating an industrial production digital prototype, which can be used as a tool at the design and management stages of its functioning, as well as its application in the educational process while training highly qualified technical specialists -during lectures, practical and assessment classes with students.The virtual production space creation was carried out using the software environments Twinmotion, Bigscreen and 3D Vista Virtual Tour Pro.A digital prototype of a machine-building plant was created in the Twinmotion software environment, providing immersive architectural 3D visualization.The created virtual tour in the 3DVista Virtual Tour Pro environment is used for a variety of tasks: online discussion of the designed equipment 3D model; staff training; emergency situations drills; completing onsite educational quests.The Live Guided Tours tool included in 3DVista Virtual Tour Pro allows to conduct real time video conferences inside a virtual tour.The use of a multi-user social VR application for virtual reality Bigscreen provides network participants with the opportunity to meet and interact through avatars inside virtual reality rooms.Thus, the authors proposed an approach to the digital transformation of industrial production into the educational process.It allows us to provide conditions for improving the education quality, exchanging opinions, and mutual counseling.Visualization of a digital prototype of industrial production is available to all Internet users.
Efficient Clustering for Continuous Occupancy Mapping Using a Mixture of Gaussian Processes

This paper proposes a novel method for occupancy map building using a mixture of Gaussian processes. Gaussian processes have proven to be highly flexible and accurate for a robotic occupancy mapping problem, yet the high computational complexity has been a critical barrier for large-scale applications. We consider clustering the data into small, manageable subsets and applying a mixture of Gaussian processes. One of the problems in clustering is that the number of groups is not known a priori, thus requiring inputs from experts. We propose two efficient clustering methods utilizing (1) a Dirichlet process and (2) geometrical information in the context of occupancy mapping. We will show that the Dirichlet process-based clustering can significantly speed up the training step of the Gaussian process and if geometrical features, such as line features, are available, they can further improve the clustering accuracy. We will provide simulation results, analyze the performance and demonstrate the benefits of the proposed methods.
Research on the Design of Bright Clothing for the Elderly Based on Intelligent Detection of Lower Limb Posture Antifall Sensors

In this paper, we construct a model for intelligent detection of lower limb posture fall prevention sensors, conduct an in-depth study of bright clothing for the elderly with smart detection of fall prevention, and design an article of colorful clothing for the elderly based on intelligent detection of lower limb posture fall prevention sensors. In this paper, the overall system scheme is determined according to the application, including the characteristic position of the device to be worn, the establishment of the reference coordinate system, and the setting of the overall system architecture. The data acquisition, transmission, and saving functions are realized, and the two-step extended Kalman filter, complementary filter, and DMP-Kalman filter are used to solve the posture angle of the measured object. The feasibility of the prototype was verified by testing the daily behavioral activity data and fall behavior data of the elderly, and the collected data were processed and analyzed by three data fusion algorithms, which proved the effectiveness of the algorithms, among which the DMP-Kalman filter algorithm has better performance. From the test results, the design of this paper can detect the posture angle of the human body more accurately, which provides a practical reference for fall detection equipment. Using clothing as a carrier, the fall detection and warning module is integrated with clothing to study an article of intelligent clothing for fall detection and warning for the elderly. A core issue in the integration process is the influence of the clothing on the detection module when the human body is moving. The effect of the material and loft of the clothing on the model is studied through experiments. The results of the two indexes, i.e., sensitivity and specificity, of the integrated model and the model not integrated with the clothing are around 98%. The experimental results of the material and loft of the clothing within a specific range have little influence on the model. The final intelligent garment design scheme was determined on this basis.
Virtual Reality and Augmented Reality to Fight Effectively against Pandemics

Virtual reality creates an environment with which the user can interact. Virtual reality therefore artificially reproduces a sensory experience, which may include sight, touch, hearing and smell. “The resulting AIP-cube provides a useful qualitative tool for describing, categorizing, comparing, and contrasting virtual environments, as well as more conventional computer animation and graphic simulation systems”. Laval Virtual is a major and historic association in the field of immersive technologies: VR, AR, and mixed reality. Its collaborators and networks of experts and partners are committed to enabling professionals to meet at leading international trade shows, attend leading international conferences, or participate in theme days. In VR, headsets immerse users in a 3-D computer-generated environment where spatial data, such as the data that produces a rendering of a building or a house, become more real and more actionable. While VR is often associated with gaming and entertainment, VR applications for business are expanding strongly, they are used in various sectors, including health.
Application of Knowledge Model in Dance Teaching Based on Wearable Device Based on Deep Learning

With the great changes in the social, economic, and cultural background, the traditional education model can no longer meet the current goal of cultivating dance talents. The application of wearable devices based on deep learning helps to improve students’ understanding and application of dance movements. Through extensive data analysis, experimental research, and kinesthetic theory, it is found that trainers based on deep neural networks can effectively improve students’ overall learning performance. At the same time, the application of emotion-intelligence teaching mode theory in dance experiments and the conclusions drawn from the experimental research fully demonstrate the teaching advantages of applying deep learning wearable devices to dance emotion-intelligence teaching mode. In order to explore the application of wearable devices based on deep learning in dance teaching, this paper discusses the improvement of deep learning through the elaboration of parameter smoothing initialization, convolutional pooling layer, optimal smoothing filter, and dynamic pruning method, and then a wearable device is designed. A device can recognize dance movements to verify the application of deep learning-based wearables in dance teaching in emotional mode.
Semantic Representation Using Sub-Symbolic Knowledge in Commonsense Reasoning

The commonsense question and answering (CSQA) system predicts the right answer based on a comprehensive understanding of the question. Previous research has developed models that use QA pairs, the corresponding evidence, or the knowledge graph as an input. Each method executes QA tasks with representations of pre-trained language models. However, the ability of the pre-trained language model to comprehend completely remains debatable. In this study, adversarial attack experiments were conducted on question-understanding. We examined the restrictions on the question-reasoning process of the pre-trained language model, and then demonstrated the need for models to use the logical structure of abstract meaning representations (AMRs). Additionally, the experimental results demonstrated that the method performed best when the AMR graph was extended with ConceptNet. With this extension, our proposed method outperformed the baseline in diverse commonsense-reasoning QA tasks.
An interpretable uni-nullneuron-based evolving neuro-fuzzy network acting to identify Dry Beans

Evolving systems are models which are able to act dynamically in adaptive open-loop manner for solving data stream modeling problems within different application areas. Their parametric adaptability for architectural constructions of models allows them to flexibly solve issues of the most varied natures. Data mining problems in the agriculture area are the target of recent researches, mainly through extracting the basic characteristics from images to transform them into feature data that can be processed by machine learning approaches. This work aims to address the problem of identifying dry beans with interpretable models and results. For this purpose, an evolving neuro-fuzzy network based on uni-nullneurons, capable of extracting knowledge about a data set through if-then rules, was used in this paper. The data set used in this work was the subject of several approaches in the literature, and the results obtained (reaching about 98.18% classification accuracy) prove that the evolving neuro-fuzzy network used in this paper can identify dry seed beans with a high degree of precision while allowing the interpretation and dissemination of knowledge about their correct identification.
68MO Generalization of a deep learning model for HER2 status predictions on H&amp;E-stained whole slide images derived from 3 neoadjuvant clinical studies

Due to its decisive role to select targeted treatment options in breast cancer, we investigated if machine learning (ML) models could identify HER2-positive tumours from routinely obtained haematoxylin and eosin (H&E)-stained whole slide images. We trained a machine learning model to predict the HER2 status on 844 patients from the GeparSixto and GeparSepto studies and validated it on 1567 independent patients from GeparSixto, GeparSepto and GeparOcto. The challenge of a single label per gigapixel image without additional, granular domain-expert annotations, is addressed by aggregating learned features per patient. We trained a network with a gated attention head and patch filtering on previously segmented tumour regions. Final predictions are made by an ensemble from a 5-fold outer cross-validation. Additionally, we use selective prediction analysis to choose a subset of patients for which the model is highly certain. Our model was evaluated on 1488 patients of the validation set (excluding 5% due to automatic quality control). With respect to the centrally assessed clinical HER2 status it achieves an area under the receiver-operator curve (AUROC) of 0.81 (95% CI 0.79-0.83) and a balanced accuracy (BA) of 73.1%. In the held-out GeparOcto subcohort, we observe AUROC=0.80 (95% CI 0.77-0.83) and BA=70.4%. Taking the model's confidence into account, we show that for a subset of 13% and another set of 32% of selected cases the BA increases to 84.7% and 81.5%, respectively. Considering different thresholds illustrates the trade-off between coverage and performance. The trained ML model predicts HER2 status with state-of-the-art performance. We extend on that by demonstrating the generalization of performance on a held-out clinical study (GeparOcto). In addition, our approach demonstrates that substantial performance increases can be achieved for subsets of patients based on the model's confidence. Both, the generalization to a held-out clinical study as well as the subset analysis of the most confident predictions, demonstrate the robustness of our approach.
How Adversarial Assumptions Influence Re-identification Risk Measures: A COVID-19 Case Study

The COVID-19 pandemic highlights the need for broad dissemination of case surveillance data. Local and global public health agencies have initiated efforts to do so, but there remains limited data available, due in part to concerns over privacy. As a result, current COVID-19 case surveillance data sharing policies are based on strong adversarial assumptions, such as the expectation that an attacker can readily re-identify individuals based on their distinguishability in a dataset. There are various re-identification risk measures to account for adversarial capabilities; however, the current array insufficiently accounts for real world data challenges - particularly issues of missing records in resources of identifiable records that adversaries may rely upon to execute attacks (e.g., 10 50-year-old male in the de-identified dataset vs. 5 50-year-old male in the identified dataset). In this paper, we introduce several approaches to amend such risk measures and assess re-identification risk in light of how an attacker's capabilities relate to missing records. We demonstrate the potential for these measures through a record linkage attack using COVID-19 case surveillance data and voter registration records in the state of Florida. Our findings demonstrate that adversarial assumptions, as realized in a risk measure, can dramatically affect re-identification risk estimation. Notably, we show that the re-identification risk is likely to be substantially smaller than the typical risk thresholds, which suggests that more detailed data could be shared publicly than is currently the case.
IMPACT OF NATURAL LANGUAGE PROCESSING ON CRM AND TRUST: AN INTEGRATED FRAMEWORK

Natural language processing (NLP) is an interdisciplinary concept with anecdotal studies in many fields.A limited number of studies have empirically investigated NLP applications in several industries.This research is intended to fill this gap in the literature.Using traditional tools to interact with customers in their natural languages creates misinterpretations and increases cost in language processing.This study proposes NLP techniques to minimize human factor errors in natural language processing and build multilevel relationship trust.Therefore, a sentiment analysis-based literature review is conducted to examine customer relationship management (CRM) empowered by NLP that offers a new way of building trust with customers effectively.More specifically, sentiment polarity was used to separate the favorable and unfavorable views of NLP applications.We found an integrated framework showing pillars of artificial intelligence such as machine learning, deep learning, and NLP.We also found that Data warehouse and Hadoop feed the framework which relates CRM to Trust.Future research can be used to test the framework using statistical inferences.Theoretical contributions and practical contributions are offered.
D2TS: a dual diversity tree selection approach to pruning of random forests

Abstract Random Forest is one of the most effective classification techniques. It is an ensemble technique with typically decision trees as its classifiers. Each tree votes for an outcome when a new instance is being classified, and a majority vote is taken to decide the final output. Two main factors play an essential role in Random Forests performance, namely diversity among trees in the forest and their number. Higher diversity increases prediction accuracy, whereas lower numbers of trees result in faster predictions. This paper aims at optimizing these two factors by using clustering analysis of trees in order to prune correlated trees while keeping outlier trees to maintain diversity. We group the trees into clusters and only take a number of representatives from each cluster while also keeping some or all of the outliers to preserve diversity. The resulting subset of trees will constitute a random forest of a reduced size. We will use the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering. DBSCAN is one of the most commonly used clustering techniques and is immune to outliers. We will use DBSCAN to (i) group trees in clusters based on their prediction behaviour and (ii) identify outliers. Each of the clustered and outlier trees bring an element of diversity into the pruned random forest, thus giving our approach its dual diversity aspect. Our approach achieved up to a 99% pruning level while resulting in similar, or even better, accuracy compared to the original forests for 19 public datasets with varying properties. Our source code is publicly available on GitHub.
Toward a Better Understanding of Mobile Users’ Behavior: A Web Session Repair Scheme

Using mobile devices to browse the Internet has become increasingly popular over the years. However, the risk of being exposed to malicious content, such as online scams or malware installations, has also increased significantly. In this study, we collected smartphone data from volunteer users by monitoring their use of the Web and the applications they install on their devices. However, the collected data is sometimes incomplete due to the technical limitations of mobile devices. Thus, we propose a data repair scheme to restore incomplete data by inferring missing attributes. Here, the restored data represent the browsing history of a mobile user, which can be used to determine if and how the user has been the victim of web or mobile-specific attacks to compromise their sensitive data. The accuracy of the proposed data repair scheme was evaluated using a machine learning algorithm, and the results demonstrate that the proposed scheme properly reconstructed a user’s browsing history data with an accuracy of 95%. The usability of the repaired data is demonstrated by a practical use case. The user’s browsing history was correlated with other types of data, such as received SMSs and the applications installed by the user. The results demonstrate that a user can fall victim to SMS-based phishing (SMShing) attacks, where the attacker sends an SMS message to a user to trick them install a malicious application. We also present a case of a social engineering attack, where the victim was manipulated to provide their Amazon credentials and credit card details.
Electronic Nutrition (e-Gizi) Application Development on RSUP Dr. Mohammad Hoesin Palembang Using the Rapid Application Development Method

Regional General Hospital Dr Mohammad Hoesin Palembang is one of the types A vertical hospitals in Palembang. Patients who come are not only from South Sumatra but also from outside the region. Dr Mohammad Hoesin Hospital has inpatient services. One of the inpatient facilities is providing food which will be divided into 3 shifts. Currently, data gathering on the patient diet is performed manually, which indicates that many patients have returned home but somehow the food has been made, and the natural ingredients at the nutrition establishment do not match what would be in it. A solution that can facilitate the distribution of food at Dr Mohammad Hoesin Hospital and make it easier to record the patient's diet is to build a website-based Electronic Nutrition (e-Gizi) application.
FisicARtivo: Design of a learning tool for physics education using web-based XR technology

Currently, the development of educational technology seeks to improve the learning process of students through applications and platforms that offer interactive and intuitive experiences. This article presents the design of an interactive learning tool called FisicARtivo, which is focused on teaching physics with real-time simulations in extended reality (XR), using technologies such as augmented reality (AR) and virtual reality (VR) for the presentation of interactive learning environments. Preliminary results obtained in this work show that the use of extended reality technologies has a significant impact on physics learning.