Reviewerly/exHarmony huggingface dataset:
Research on USV Path Planning Method Based on Improved Option-Critical Algorithm

In hierarchical reinforcement learning, time abstraction is the key to enhance the learning ability and planning ability of agents. Option-Critic provides a framework for defining action plans with time abstraction ability. In the past ten years, autonomous learning time abstraction has been widely studied by scholars. The algorithm has the advantages of flexibility and efficiency, and has achieved good results in the classic four room navigation task and yadali game. However, option-critic still has the disadvantage of converging to a single action. At the same time, there is a big gap between the four room navigation task environment and the water surface environment during Unmanned Surface Vehicle (USV) navigation. In order to apply the option-critic framework to the path planning task of USV, this paper improves it, and changes the neural network of the feature extractor into a recurrent neural network. The network input is the action state sequence of unmanned craft, in order to extract as many environmental state features as possible. Through simulation experiments, the feasibility and effectiveness of the algorithm to realize route planning are tested in a more complex unknown map environment.
Making Images Resilient to Adversarial Example Attacks

Adversarial example attacks twist an image to cause image classifiers to output a wrong prediction, yet the perturbation is too subtle to be perceived by a human. Existing research has focused on improving the accuracy of image classifiers as a defense. In this paper, we consider the problem of thwarting adversarial example attacks from a different aspect. Instead of developing better image classifiers, our idea is to make images themselves more resilient to the attacks. Specifically, we propose to convert an image into an adversary-proof example to have three properties: 1) The modification is barely noticeable to human eyes; 2) The new image will receive same predictions from image classifiers; and 3) It is much harder for one to compute an adversarial example from the new image than from the original one. We present two solutions to compute adversary-proof examples, and evaluate their performance with two datasets, MNIST and CIFAR10. Our results show that the concept of adversary-proof example can indeed serve effectively as the first line of defense against adversarial example attacks.
Cross-Domain Learning for Reference-Based Sketch Colorization with Structural and Colorific Strategy

This paper aims to tackle the colorization task of sketch image given an already-colored reference image. Sketch colorization is a thorny task for computer vision since neither grayscale values nor semantic information exists in sketch images. To address this, We propose to jointly train the domain alignment network with a simple adversarial strategy, that we term the structural and colorific conditions, to learn the semantical correspondence between information-scarce sketch and the given instructive reference. Specifically, the inputs from distinct domains will be aligned to an embedding space where the semantical correspondence is established, then, the generator will reconstruct the sketch image according to the established correspondence. We demonstrate the effectiveness of our proposed method in sketch colorization tasks via quantitative and qualitative evaluation against existing approaches in terms of image quality as well as style relevance.
Ensemble of One-Class Classifiers Based on Multi-level Hidden Representations Abstracted from Convolutional Autoencoder for Anomaly Detection

Image anomaly detection has recently emerged a large number of methods, which are widely used in industry, medicine and other fields. In this paper, we propose a novel image anomaly detection method, named Ensemble of One-Class Classifiers based on multi-level hidden representations abstracted from convolutional autoencoder (EOCCA), which is a two-stage method. Specifically, in the training process of the model, first, we train the convolutional autoencoder with the goal of minimizing the reconstruction error, thereby extracting the features from different layers of the encoder. Second, we train multiple one-class support vector machines based on the multi-level hidden representations abstracted from convolutional autoencoder. During inference, we ensemble all trained one-class classifiers for anomaly detection. Experimental results on several image anomaly detection benchmark datasets demonstrate that our proposed method is on par or outperforms current state-of-the-art image anomaly detection methods.
Blockchain based Scalable model for Secure Dynamic Spectrum Access

Conventional Cooperative spectrum sensing techniques either suffer from single point of failure attack or lack in providing incentives to users which makes them incompatible for Wireless Service Provider (WSP). We propose a dynamic spectrum access framework for WSP which gives prominence to automated sensing and sharing with the use of blockchain. In this system, the opportunity of spectrum access is first examined by sensor nodes and the access right is then allocated to the users when their transactions to WSP are authenticated in a decentralized manner. Apart from using blockchain as a reliable platform for automatic enforcement of spectrum sensing, we propose a novel mechanism for securing our network from the threats designed primarily for Cognitive Radio Networks. In addition to this, our proposed approach enhances the scalability of blockchain networks by using the sidechains for storing data and checkpointing it onto main chain after periodic intervals of time. Extensive simulations in Octave indicate superior performance offered by our proposed model.
Design and Development of a Dual-Axis Solar Tracking System with Enhancements

Development of a dual-axis solar tracking system is more complex than a single-axis solar tracking system, but a dual-axis system tracks much better as compared to a single-axis system. The aim here is to design and develop a real model for dual-axis solar tracking system that has two degrees of freedom. In this work, a mathematical model of the dual-axis system has been developed using the bond graph approach. Simulation of the system from its bond graph model is done using MATLAB. The tracking mechanism in this system does not depend on the exact position of the sun. The system moves around the azimuth and elevation axes while tracking the highest intensity falling on the light-dependent resistor (LDR) sensors, allowing it to track the sun precisely. It accounts for seasonal and daily variations in the sun's position; thus, there will be increased exposure of the panel to the sun, resulting in enhanced power output and improved efficiency.
An Ensembled Intense Approach for Image Inpainting

Recently there is a growing trend among researchers to work on image inpainting. Inpainting is a technique of reconstructing a damaged image in a form that is not detectable by an ordinary observer. There are different types of inpainting techniques such as Texture Synthesis, partial differential equations (PDE) and so on. These techniques follow a pixel-by-pixel approach, where at a time only one pixel’s value gets manipulated; therefore, more execution time is required. Another technique is exemplar-based, this technique follows a patch-by-patch approach where instead of replacing one pixel at a time, replaces a group/patch of pixels; therefore, less execution time is required. This paper combines basic techniques such as Texture Synthesis, PDE and coherence among neighbouring pixels with exemplar-based and pruning technique. This study presents the extended work by improving the execution time and preserve the quality of the image. Using the proposed technique execution time of the inpainting process has been decreased from minutes to seconds irrespective of machine/computer configuration.
Comparative Performance Analysis of Different Membership Functions on Fuzzy Logic-Controlled Solar Tracking System

Membership function is the chief constituent of the fuzzy set theory as they characterise fuzziness in a fuzzy set. Selecting both range and the shape of membership function plays a vital role in overall performance of fuzzy inference system. Due to the availability of a variety of membership functions, model designer generally faces difficulty in selecting the most suitable membership function for the system. The main goal of this proposed work is to carry out performance comparative analysis of a fuzzy logic controller with different membership functions and linguistic variables. Here, for the analysis purpose, the fuzzy logic controller has been designed for solar tracking system using Matlab/Simulink module.
Simulated Gaze Tracking using Computer Vision for Virtual Reality

As Virtual Reality (VR) is becoming more viable for creating immersive learning environments, a promising area of study is in monitoring and assessing learner’s engagement. Several related works in this area have used gaze tracking. Unfortunately, gaze tracking in VR is currently limited by hardware availability. This paper presents an approach for engagement monitoring that does not require the traditional costs of more sophisticated hardware setup. A simple combination of video processing techniques was used to show which contents capture the user’s attention, similar to the information current gaze trackers provide. However, success with this method was only evident with a sample video; refinements are still needed to cater to unpredictable changes in field of view. After fine-tuning the process, this approach may potentially benefit instructional VR content developers in determining which content grabs their learners’ attention effectively.
Using First-Person View Drones through Head-Mounted Displays: Are They Suitable for Education?

This study examined in-service teachers’ perceptions regarding spatial presence, simulator sickness and usability of First-Person view drones through Head-Mounted Displays in order to determine the suitability of their use in teaching and learning processes. The sample consisted of 60 in-service teachers of primary education. Data was collected via the Temple Presence Inventory Scale, Simulator Sickness Scale and the System Usability Scale. Results showed that the teachers rated an increased level of spatial presence. Additionally, the simulator sickness was relatively low, and the drone’s usability was rated as excellent. These findings contribute to the better understanding of the potential of First-Person view drones as learning tools.
Combining Attention-based Models with the MeSH Ontology for Semantic Textual Similarity in Clinical Notes

In this study, we present several transformer-based models as well as traditional machine learning methods to detect semantic textual similarity (STS) in clinical notes. We investigate transformer models pretrained on general English as well as clinical notes, and use generic English STS datasets as a supplemental corpus to clinical notes data. Our work is based on the 2019 National NLP Clinical Challenge (n2c2). We identify and annotate six types of sentences in the clinical notes corpus, and report an ensemble method that combines attention-based contextualized embeddings with a similarity score based on the MeSH ontology obtained by computing least common ancestors of clinical terms. Our approach does not need additional clinical data for model training, while still achieving comparable Pearson's correlation coefficient of 0.901.
An Augmented Reality Training Application for Service and Maintenance of a Medical Analyzer: A UX Approach to Usefulness and User Satisfaction

This paper presents an experimental study on a mobile augmented reality application for immersive training of field engineers in service and maintenance of a medical analyzer (AQT90FLEX). Based on approaches from user experience design, we developed an AR training application with the aim of high level of relevance, ease of use, usefulness, user satisfaction, and learnability. Sixteen field engineers from the multinational company Radiometer participated in the study. The procedure was divided into three iterative stages: design, prototype, and evaluation. The methods consisted of questionnaires, interviews, and co-creation. The questionnaire was inspired by the technology acceptance model. The findings revealed that all the field engineers expressed positive feedback in terms of being able to see, train, and practice on the AQT90FLEX analyzer. Especially the usefulness and user satisfaction were positively evaluated. This study also shows the importance of using mixed methods, both qualitative and quantitative approaches, in order to develop an AR training application for field engineers across the world.
A Conceptual Learning Framework of Cybersecurity Education for Military and Law Enforcement

To address cybersecurity threats that organizations are facing today, there is an urgent need for an interdisciplinary approach in educational programming to prepare the next generation of indispensable workers who are often dispersed, such as law enforcement and military personnel. Extensive data breaches and even low profile but high impact cybercrimes present immense challenges for law enforcement, military, and local government agencies. These agencies, by nature, are some of the primary targets of cyberattacks, and hence, cybersecurity awareness and cyber investigation-related education are crucial for meeting the demanding requirements of their job duties and responsibilities. This paper describes the pedagogy of current educational programs for military and law enforcement toward identifying existing gaps in the adult pedagogy used to prepare the workforce. The paper concludes with a proposed framework based on recommendations on domain-specific topics and pedagogical formats for the most effective cybersecurity learning for these dispersed groups.
Sanity Check for Shapley values-based explanations of Deep Neural Networks Predictions

Explanation methods have been introduced to un-cover the black-box of Deep Neural Networks (DNNs). The interpretability of the prediction is crucial in medicine, where the use of black-box algorithms has an impact on human life. An important class of explanation methods is based on the Shapley values. Since no ground truth explanation is available, assessing the explanation faithfulness is difficult. In this paper, a comparison among Shapley values-based explanations is performed, and a sanity check of their faithfulness from the literature is applied. The results show that these methods pass the check and they can be suited to explain DNN in healthcare scenarios.
NLP System for Mining Social Determinant of Health From Clinical Notes and its Fairness Evaluations

This research proposal talks about recognizing SDoH components from clinical text using machine learning approaches. Upon developing a best model in practice for the task, existing tools are employed to assess and recalibrate machine biases. This study plans to use housing related entities as start point.
Graph-Augmented Cyclic Learning Framework for Similarity Estimation of Medical Clinical Notes

Semantic textual similarity (STS) in the clinical domain helps improve diagnostic efficiency and produce concise texts for downstream data mining tasks. However, given the high degree of domain knowledge involved in clinic text, it remains challenging for general language models to infer implicit medical relationships behind clinical sentences and output similarities correctly. In this paper, we present a graph-augmented cyclic learning framework for similarity estimation in the clinical domain. The framework can be conveniently implemented on a state-of-art backbone language model, and improve its performance by leveraging domain knowledge through co-training with an auxiliary graph convolution network (GCN) based network. We report the success of introducing domain knowledge in GCN and the co-training framework by improving the Bio-clinical BERT baseline by 16.3% and 27.9%, respectively.
A Mixed Reality Teaching Course for Formal Higher Education

The advancements of mixed reality technologies enable a productive application of mixed reality in various fields. This increases the demand for skilled mixed reality developers. Current curricula do not offer dedicated mixed reality courses in higher education that provide the necessary competences. We demonstrate a lab course for mixed reality software engineering in a bachelor program at a technical university with its learning goals and the identified skills to support future mixed reality computer scientists. The course was conducted five times over two and a half years with a total of 38 students. In all instances, the same open-source software project was used as a boundary object to train agile software engineering, mixed reality development competences, and presentation skills. As the course teaches state-of-the-art agile workflows, its aim is to prepare students for scientific projects, a bachelor thesis and industry training scenarios in mixed reality.
Scaled Agile Framework for Achieving Speed and Customer Satisfaction at a Large Scale

The main goal of this paper is to identify challenges organizations are facing and then to address the fact that on a larger scale the issues will only become greater and more complicated. As a solution to those challenges one of the arising frameworks for scaled Agile - SAFe 5.1 was described in detail.
Work-in-Progress—Decolonizing the Digital Divide: Problem Based Spatial Design Through Immersive Technology for STEM Education in Minority Populations

This work-in-progress paper reports preliminary findings from surveys, participant observation, and co-design discussions with educators and elders of a Native American community about how to modify STEM learning activities for their unique tribal culture in afterschool settings using immersive technology and spatial design.
Work-In-Progress—A Mobile Augmented Reality Application for Learning about Trademark Registration in Intellectual Property Education

Intellectual Property (IP) education plays a key role for support and creativity in society. However, research on IP education has not paid attention to the potential that AR could bring to this field. This work-in-progress paper presents an augmented reality mobile application developed in Unity for learning trademark registration as part of the first phase of a research project studying the uses of AR in intellectual property education. This paper describes the design of the application and some screenshots of the final developed version, the research method, instruments to evaluate the application, and preliminary findings. The main contribution of this paper is a use case of AR in the field of intellectual property education, an example of designing an app for this topic and some insights into the benefits of AR in the field of intellectual property education.
Reflecting on Research: a Virtual GLAM Proposal

Galleries, Libraries, Archives, and Museums (GLAM) are known to embrace new technologies so they can offer a more informative visitor experience, merging physical artifacts with supporting information offered in a digital format. It is proposed that university and corporate research laboratories can take the form of a GLAM if appropriated as a virtual entity. A virtual GLAM can subsequently support a holistic overview of research projects undertaken by associated researchers to ensure academic affinity with the overall research theme and facilitate the planning of future research. The paper describes the development of a virtual GLAM as a Case Study and adopts reflective practice as a lens through which to evaluate 12 years of research projects.
Work-in-Progress—Measuring Learners’ Subjective Experience in Augmented Reality: First Evaluation of the ARcis Questionnaire

In order to effectively use augmented reality (AR) for learning support, it is important to take into account learners’ subjective AR-specific learning experience. In this work-in-progress paper, a first version of the ARcis questionnaire intended to measure learners’ perception of contextuality, interactivity, and spatiality in AR-based learning environments is presented. Results of a first implementation of this questionnaire in four studies (total N = 456) with different AR-based learning materials are described. Analyses of reliability and factor structure of the questionnaire show promising first outcomes on which suggestions for adaptations and future validation strategies are made.
On the Suitability of Cloud Computing as an Information Aggregating Environment from Monitoring Systems

The advantages of Cloud Computing are indisputable, when fast and efficient solutions are sought with flexible, scalable access to specialized resources (technologies, systems, etc.) and the predominance of operating costs over capital ones. Their careful and correct application would lead to competitive advantages in business. This paper presents some results from one approach to the application of Cloud Computing for connectivity tests of monitoring systems for material objects. It is clear from them that cloud technologies are suitable as an information aggregation environment from such systems.
Teaching and Learning Immersive Technology in Education Sciences

The immense potential of the immersive technology application in education has irreversibly shifted the status quo of pedagogic emphases across the disciplines and all levels of education. This shift, in turn, demands Higher Education in education sciences to adapt to these changes and to provide programmes and courses, where future specialists and researchers in such fields as technology enhanced learning, education technology solutions and applications can gain their expertise and qualifications. This research was conducted as a case study analysis of teaching and learning immersive technology in the field of education sciences at a university Masters level course “Virtual and Augmented Technology for Education”. This paper analyses teaching approaches and learner experience during two consecutive course years in order to formulate the findings and conclusions valuable for future application in the field of education sciences and potentially more broadly across the disciplines.
Work-in-Progress-Volumetric Communication for Remote Assistance Giving Cardiopulmonary Resuscitation

We present our work in progress, a real-time mixed reality communication system for remote assistance in medical emergency situations. 3D cameras capture the emergency situa-tion and send volumetric data to a remote expert. The remote expert sees the volumetric scene through mixed reality glasses and guides an operator at the patient. The local operator receives audio and visual guidance augmented onto the mixed reality headset. We compare the mixed reality system against traditional video communication in a user study on a CPR emergency simulation. We evaluate task performance, cognitive load, and user interaction. The results will help to better understand the benefits of using augmented and volumetric information in medical emergency procedures.