Reviewerly/exHarmony huggingface dataset:
An eye tracking experiment investigating synonymy in conceptual model validation

A key advantage of conceptual models is that their quality can be evaluated and validated before beginning the costlier stages of information system development. Few research studies investigate the validation process for such models, particularly regarding multiplicities, even though multiplicity mistakes can be very costly. We investigated the validation of conceptual model multiplicities, varying how closely natural language statements of business rules match the models that purport to represent those rules. Participants in an eye tracking experiment completed validation tasks in which they viewed a statement and an accompanying UML class diagram in which a specified multiplicity was consistent with the statement (valid) or inconsistent with the statement (invalid). We varied whether the focal multiplicity was a minimum or a maximum and varied the class diagram’s semantics and order compared to that of the statement. Logistic regression was used to analyze the relationship between accuracy and the experimental manipulations and controls. The results show that the odds of accuracy in validating class diagrams that used synonyms instead of the exact statement terminology were only 0.46 times the odds of accuracy when the class diagram and statement words matched, showing a costly effect of synonymy. Interestingly, independent of the three levels of relative semantics, the odds of accuracy were 0.48 times when class diagrams were consistent with business rules as they were when class diagrams were inconsistent with business rules. To gain insight into cognition under correct task performance, we conducted additional linear regression analysis on various eye tracking metrics for only the accurate responses. Again, synonymy was observed to be costly, with a cognitive burden of increased integrative transitions between statement and model in the range of 39 to 66%.
Application of Image Processing Technology in Aerobics Injury Diagnosis

Aerobics sports injury diagnosis is a rapid diagnosis of sports injuries caused by athletes in the process of aerobics training or competition. The purpose of this paper is to use image processing technology to study and analyze the diagnosis of aerobics sports injury, so that the diagnosis results can be obtained more quickly and effectively. This paper first introduces the image processing technology and then analyzes the sports injury in colleges and universities. Firstly, the algorithm formula of image processing technology is given, and then the algorithm is introduced into the dynamic analysis of aerobics injury diagnosis. The two are compared through example analysis. The experimental results show that joint strain, sprains, and muscle strain are the main types of sports injury of College Aerobics students, reaching 59, 50, and 31 times, respectively. When using image processing technology in the diagnosis of sports injury, the diagnosis results can be obtained quickly and effectively.
A Regularization-Based Long Short-Term Memory Architecture Design for Solar Irradiance Prediction

As environmental concerns related to energy generation grow, alternative solutions to carbon-based electrical generation are becoming ever critical to grid operations. Photovoltaic (PV) panels are effective in converting clean solar energy to electrical power, enabling solar to play a central role in meeting grid demand. PV deployment has increased exponentially, requiring a smart power Distribution Network (DN). However, due to its dependence on weather parameters, including solar irradiance, PV power is inherently intermittent in nature. The ability to deliver an accurate forecast of solar irradiance would facilitate the integration of PV within a decentralized grid network. Utilising raw and engineered data features from a real-world campus-based PV system at the Nimbus Research Centre, this research examines the Long Short-Term Memory algorithm with dropout regularisation (R-LSTM), as a suitable day-ahead irradiance forecast model. Adopting $R^{2} $ and MAE as evaluation metrics, the forecast results were benchmarked against a persistence model as well as a Fully Connected Artificial Neural Network (FCANN) model. Simulation results demonstrated that the proposed RLSTM model outperformed the FCANN algorithm by 5.0% ($R^{2}$ score) and 4.7% (MAE) respectively. Additionally, it showcases that the proposed architecture is competent in observing the complex relationship between candidate input features and solar irradiance.
Using Super Resolution Perception for Anomaly Detection in Energy Load Profiles

Detection of anomalies in energy load profiles is crucial for the optimization of energy resources. Although there are many possible approaches to anomaly detection, in this paper it is proposed a novel approach based on the use of an artificial neural network already trained to solve the problem of Super Resolution Perception (SRP) as a method to detect anomalies in energy consumption. Furthermore, a comparison with a conventional autoencoder (AE) is provided to demonstrate that the solution to the SRP problem generates a competitive model which surpasses in several metrics to AE in the anomaly detection task without additional implementation costs.
Differentially Private <i>k</i> -Nearest Neighbor Missing Data Imputation

Using techniques employing smooth sensitivity , we develop a method for \( k \) -nearest neighbor missing data imputation with differential privacy. This requires bounding the number of data incomplete tuples that can have their data complete “donor” changed by making a single addition or deletion to the dataset. The multiplicity of a single individual’s impact on an imputed dataset necessarily means our mechanisms require the addition of more noise than mechanisms that ignore missing data, but we show empirically that this is significantly outweighed by the bias reduction from imputing missing data.
Convolutional Neural Network Models for Throat Cancer Classification Using Histopathological Images

Cancer is a condition where the abnormal growth of cells is observed, initially identified as tumors. Cancer can happen to any part of the body, early detection of cancer can increase the life expectancy of patients in this paper throat cancer is considered for the study, cancer can start at any part of the throat and later stage can spread to the lungs and liver. Samples are extracted through the fine needle biopsy method and by using Hematoxylin, Eosin (H&E) stain images are obtained with three magnitudes original, 10 $$\times$$ , 20 $$\times$$ from an electron microscope. Convolution Neural Networks (CNN) with Support Vector Machine (SVM) and Transfer Learning (TL) are used to classify the images as either cancer or normal both techniques have shown promising results with an average accuracy of 93%. Transfer learning with 10 $$\times$$ data has provided better results with an average accuracy of 94.18% as TL slightly outperformed SVM and itself with other magnitudes.
Maturity of Artificial Intelligence in SMEs: Privacy and Ethics Dimensions

Artificial intelligence (AI) remains volatile for many companies and will foster disruptive transformation processes in many industries. Companies face continuous challenges assessing AI-readiness, current state, and AI-based requirements for change. Assessment of AI applications, services and products can be built on maturity models (MM). In this case, specialized AI-based MM (AIMM) are needed. With this article, we intend to advance the AIMM development. Aspects of small and medium sized companies (SME), privacy, and ethics are in focus. We will present the current state of AIMM, its interim, and practical evaluation and an outlook on further AIMM developments.
Hand-Eye-Object Tracking for Human Intention Inference

Optimizing human-robot interaction in the performance of collaborative tasks is a challenging problem. An important aspect of this problem relates to the robot's understanding of human intentions. Empowering robots with accurate intention inference capabilities sets the stage for more natural, safe, and effcient interactions and greater confidence in the Human-Robot Interaction domain. Intentions can be deduced by observing human cues as they interact with the environment, but currently, there is no clear-cut method for achieving this in an effective way. Here, we present a novel method for intention inference based on the integration of three visual cues, namely, hand movement, eye fixation, and object interaction, coupled with a bidirectional LSTM neural network for the classification of human intention. Experimental studies evaluating our approach against two-visual cue alternatives confirm the utility of our approach.
Some Observations on Lazy FSCA and its Performance Bounds

Greedy search algorithms are a practical approach to approximating the solution of optimal subset selection problems, such as selecting the optimum model inputs from a set of candidate features. When a submodularity property holds for the feature selection metric, an efficient ‘lazy’ implementation can be employed. Recently, the authors have shown that a lazy implementation of Forward Selection Component Analysis (Lazy FSCA) yields comparable performance to FSCA, even though its selection metric (variance explained) is not submodular. In this paper we empirically investigate the sensitivity of Lazy FSCA to submodularity violations and provide a comparative assessment of a number of theoretical greedy search performance bounds that apply to FSCA. Using a diverse range of real world datasets and extensive Monte Carlo simulations we show that even though violations in submodularity can be frequent, the impact on variance explained tends to be minimal. This is true even when there are large deviations in the sequence of selected variables, which can arise with highly correlated datasets. In addition, the available performance bounds are shown to be very conservative and a poor reflection of the true performance of FSCA/Lazy FSCA.
Exploration-Based Search for an Unknown Number of Targets using a UAV

We consider a scenario in which a UAV must locate an unknown number of targets at unknown locations in a 2D environment. A random finite set formulation with a particle filter is used to estimate the target locations from noisy measurements that may miss targets. A novel planning algorithm selects a next UAV state that maximizes an objective function consisting of two components: target refinement and an exploration. Found targets are saved and then disregarded from measurements to focus on refining poorly seen targets. The desired next state is used as a reference point for a nonlinear tracking controller for the robot. Simulation results show that the method works better than lawnmower and mutual-information baselines.
Defining a Context Model for Smart Manufacturing

In smart manufacturing, automatically detecting and controlling a situation becomes more and more important. During a production, a variety of machine errors and failures may occur. In order to take further measures and decisions, context information is important to understand the situation and to identify the need for action. Context information enables decisions and allows processes to adapt as precisely as possible. The development of a suitable context model for smart manufacturing is a very complex task. In this paper, we describe an approach for the development of a semantic context model and present use cases that show, how context information can be used for smart manufacturing. In an ongoing research project about self-adaptive production processes, we categorized context information based on selected use cases and complemented the results in an entity model. We implemented this context model for a lab-based smart manufacturing prototype, using a context data storage and an annotation component. For the context model, different submodels, such as the entity and instance model, have been developed. In the implementation, the context model is the basis for flexible and self-healing processes, which will be evaluated in use cases. The result of our context modeling process is an OWL context model for smart manufacturing.
Evolution of Pick-by-Light Concepts for Assembly Workstations to improve the Efficiency in Industry 4.0

The digitization and optimization of processes in the industry 4.0, especially in the context of assembly, is becoming more and more important. To improve the link between digital information and production technology is an essential step in the digitization effort, specifically for assembly. In this context, the connection of the real and virtual world is relevant for the system to be able to react to changes in the real world and vice versa. This paper presents and discusses three sequentially developed concepts for an assembly workstation. First, we developed a pick-by-light concept with paper guidance, and then we digitized the paper guidance and displayed it using an overhead projection on the work surface. Based on these concepts, we developed a tangible interface that allows the user to interact with the virtual system using physical objects. Applying an iterative development process, we were able to continuously improve our concepts. The three concepts were each evaluated individually and compared with each other. The results provide an insight into the improvement of the efficiency in processing times, picking times and picking errors.
Targets of phishing attacks: The bigger fish to fry

Phishing attacks are one of the most common forms of information security threats. Previous research indicates that users with higher educational levels are less susceptible to phishing attacks. The paper's goal is to investigate the occurrence of phishing attacks in European countries, considering the educational level of individuals. Fuzzy clustering has been used for grouping European countries according to the educational level of individuals and the occurrence of phishing attacks. Contrary to the rationale that more educated users are less susceptible to internet fraud, the results indicate that these individuals are more susceptible to phishing attacks.
Q-Eclat: Vertical Mining of Interesting Quantitative Patterns

Frequent pattern mining is a popular technique in big data mining and analytics. It discovers frequently occurring sets of items (e.g., popular merchandise items, frequently co-occurring events) from big data found in numerous database engineered applications. These frequent patterns can be discovered horizontally by transaction-centric mining algorithms or vertically by item-centric mining algorithms. Regardless of their mining direction (horizontal or vertical), traditional frequent pattern mining algorithms aim to discover Boolean frequent patterns in the sense that patterns capture the presence (or absence) of items within the discovered patterns. However, there are many real-life situations, in which quantities of items within the patterns are important. For example, the quantity of items may also affect profits of selling the items within the discovered patterns. Hence, in this paper, we present an algorithm for vertical mining of interesting quantitative frequent patterns. This Q-Eclat algorithm first represents the big data as a collection of equivalence classes according to their prefix item labels. Each domain item is represented by one of these classes. Their corresponding item-centric sets capture (a) IDs of transactions containing the item, as well as (b) the quantity of that item in each transaction. With this representation, our algorithm then vertically mines quantitative frequent patterns. When compared the existing MQA-M algorithm (which was built for quantitative horizontal frequent pattern mining), evaluation results show that our quantitative vertical Q-Eclat algorithm takes shorter runtime to mine quantitative frequent patterns.
An Online MCQ sub-system for CrsMgr

The current pandemic has led to increased use of online learning and calls for innovative self-learning techniques. Since contact with educators is limited, students are required to become more self-reliant. This endeavour includes using self-assessment tools to measure the learning progress and uncover the areas for further studies. In this paper, we focus on a system that automatically generates various types of questions from the recommended course material. The system applies the most recent machine learning techniques, such as transfer learning, natural language generation methods and finding semantic similarity. We propose a human-in-the-loop approach where the instructor can provide his guidance. Our system would help students to calibrate themselves in a typical remote learning environment.
Distinguishing Fake and Real News of Twitter Data with the help of Machine Learning Techniques

News articles have an influence on people's belief and views about various circumstances. In this regard, some news publishers with political or ideological bias try to spread news which are distorted or totally wrong. Natural language processing was used to preprocess the text. Some general features like, number of words, sentences, stopwords, non-alphabetic words, verbs, nouns, and adjectives were identified. Word positioning was labeled to distinguish a word as a noun, a pronoun, an adjective or a verb in the sentences. Preprocessing was followed by feature extraction methods namely, count vectorizer, Term Frequency-Inverse Document Frequency (TF-IDF) vectorizer and word2vec embedding. It was observed that the results obtained by TF-IDF feature extraction method were superior compared with the other two methods. Various machine learning models were used for training the model namely, Naive Bayes, Logistic Regression, Random Forest, K-nearest neighbors (KNN), Support Vector Machine (SVM) and Recurrent Neural Network (RNN) as a deep learning model. The models were successfully tested on two datasets. On the first dataset, SVM achieved an accuracy of 98.5% and RNN achieved an accuracy of 98.03% which is much improvement over the best results of Agarwalla et al., 2019 (83.16 % accuracy). On the second dataset, SVM achieved an accuracy of 97.76%, RNN achieved 97.1% and Logistic Regression achieved 97.50% which is an improvement over the best results of Vijayraghavan et al. 2020 (94.88% accuracy).
A self-validated detector for characterization of quantum network components

We are developing a nearly polarization-independent, low-cost optical trap detector between 1000 nm and 1550 nm for optical power measurements. A NIST-traceable optical power calibration of this trap detector showed a promising result.
Wasserstein-Based Feature Map Knowledge Transfer to Improve the Performance of Small Deep Neural Networks

The Convolution Neural Network (CNN) is a class of Deep Neural Networks, specifically used for computer vision applications. The CNN architecture consists of several hidden layers. This hidden layer information, which is referred to as a feature map, contains rich spatial and semantic information about the input data. A few techniques like Knowledge Distillation, Deep Mutual Learning, and Adversarial Deep Mutual Learning have been introduced to transfer the knowledge of a large DNN to a smaller DNN for improving its prediction accuracy. However, the existing methods do not effectively use feature maps as a source of information that can be exploited to improve the performance of the small DNN. We propose an adversarial learning-based approach that consists of a simple generator and discriminator network for transferring feature map information from a large pre-trained DNN to a smaller DNN using the Wasserstein metric. Our approach helps the smaller DNNs generate feature maps similar to a large pre-trained DNN, thereby improving the accuracy and generalization ability. Our experiments show that a variety of small DNN networks benefit from the proposed approach and achieve compelling results on the CIFAR-100 and CIFAR-10 benchmark datasets. Moreover, experimental results on the MobileNet architecture illustrate that the proposed approach is particularly effective for relatively small DNN networks.
Research on the Application of Virtual Human Synthesis Technology in Human-Computer Interaction

With the rapid development of 5g, artificial intelligence, chip technology (GPU computing power), and capital investment, human body synthesis technology is entering a growth stage. According to people’s comprehensive image, we divide them into idol school, academic school, and practical school; According to the driving methods of virtual human synthesis, we divide it into avatar type, voice or text content driving type, and human-computer interaction type. We recently introduced the relevant model of the synthesis method and the implementation process of virtual human lip synthesis and explained the synthesis process. We discussed voice2face, the latest scheme for real-time synthesis of virtual human, but this method still can not synthesize high-quality video in milliseconds.
Comparison Between the Traditional and Deep Learning Algorithms on Image Matching

Image matching is the cornerstone of many computer vision tasks, which has high requirements on matching accuracy and time-consuming. The steps of extracting features, calculating descriptors, brute force matching, and filtering outliers have been integrated into a complete pipeline. This paper compares the typical traditional algorithms SIFT and RANSAC in the field of image matching with the new mainstream algorithms SuperGlue and AdaLAM proposed under the background of deep learning in recent years through experiments. Experimental results show that although traditional algorithms are still highly competitive, they are far less effective than deep learning algorithms for images with poor texture.
Improved Protocol of Yao’s Millionaires Problem

Secure multi-party computation is a hot research topic in cryptography in recent years, and has become an important research direction in cryptography. This paper studies the millionaires’ problem in secure multi-party computation, and its essence is a comparison issue about the size of two encrypted data. Yao offered a solution to the millionaires’ problem, but his protocol can’t accurately compare two numbers. We improved Yao’s protocol and proposed two generic protocols to solve the millionaires’ problem. Our first protocol has one more round of communication between participants than Yao’s, but our protocol can accurately compare the size of two numbers. The second protocol improved the first protocol, and only one round of communication is needed to get accurate comparison results. Finally, we realized the second protocol based on RSA encryption algorithm, and analyze the security under the semi-honest model.
Medicare fraud detection using graph neural networks

This study demonstrates that the performance of Medicare fraud detection models can be improved by applying graph neural networks to graph-structured datasets. Tens of billions of dollars are lost to Medicare fraud yearly. Therefore, insurance companies constantly attempt to enhance fraud detection models to alleviate economic losses and reputational damage. While traditional rule-based or machine learning models have been extensively studied for Medicare fraud detection, graph learning has been used in very few studies. Thus, a Medicare fraud detection model that applies the GraphSAGE algorithm, a graph neural network, to graph-structured datasets generated from open-source data, including the information of Medicare providers, physicians, and beneficiaries, is presented in this paper. We developed a heterogeneous graph by setting the Medicare providers and beneficiaries as nodes. Consequently, the GraphSAGE model outperformed the baseline model in terms of precision, recall, F1score, and area under the receiver operating characteristics curve by 0.01, 0.35, 0.30, and 0.18, respectively. This result indicates that the graph relationship between the provider-beneficiary and provider-physician conveys important information that can be exploited for Medicare fraud detection.