Reviewerly/exHarmony huggingface dataset:
Designing an immersive collaborative active learning experience through Phygital textbooks: Envisioning the artefact and its impact in the classrooms

Active learning in collaborative spaces (e.g., classrooms), is often supported by multiple artefacts, such as boards, textbooks, notebooks, digital devices, etc. Out of these, textbooks occupy an essential place in the teaching-learning process as they act as a static reference to the curriculum to key stakeholders such as students, teachers, and parents. To enhance the conceptual understanding of the topics through dynamic visualization of the content, there is a need for an immersive learning experience that is fostered by technology-supported collaborative active learning. This can be delivered through a phygital textbook having multiple media as per the requirement of the content. This research focuses on the design of such an experience through an interactive digital interface for physical textbooks, by employing a Design Based Research (DBR) methodology. The key focus areas for investigations are - i) the interactions and the affordances of the learner throughout the active learning process, ii) the development of a framework for the design of a phygital textbook that supports active learning in a classroom environment, and iii) understanding the factors that impact phygital artefacts based collaborative learning.
Leveraging Nonverbal Communication for Intelligent Virtual Meeting Interfaces

The prevalence of virtual meeting software and webcams have normalized holding group meetings both remotely and distributed. However, shifting group meetings virtually with typical computing setups, while convenient, also reduces fluidity of communications. That is, identifying body language of any attendee is complicated from small or lacking on-screen camera views, while distinguishing between multiple attendees’ voices is challenging with lack of spatial audio from commodity speakers and headphones. For my dissertation research, I propose designing and developing intelligent virtual meeting interfaces to narrow communication fluidity gaps between virtual meeting platforms and in-person meeting arrangements. In order to better inform hosts on attendees’ overall body language and attendees on each others’ shared responses, I explore methodologies to automatically recognize users’ physical gestures and intuitively relate them to camera video filters. Therefore, my dissertation goal is to optimally leverage nonverbal communication for improving intelligent user interfaces in virtual meetings.
Secure Real Estate Registry System using Blockchain

There are several issues with the current land registry systems that require attention. Firstly, the land registry and transfer process is currently done manually, with only some records and procedures digitized. However, this centralized system is riddled with issues, such as the inability to maintain a complete land history and the lack of a mechanism for detecting record tampering. Addressing these problems through a centralized system is not feasible, but decentralized systems such as Blockchain offer a solution with built-in tamper detection features. In this proposal, we aim to tackle the issues mentioned earlier by implementing a smart contract on the Blockchain network for registering lands. This proposed solution offers various benefits to stakeholders, including enhanced efficiency, transparency, trustworthiness, and integrity throughout the buying and selling process. Additionally, the framework provides a detailed history of the property, ensuring that records remain untampered and reliable. Furthermore, our proposed system is accessible via a Restful external link to traditional property dealing apps, allowing them to extract real-time information on the property, such as its dimensions, location, and price. Ultimately, our solution aims to instill confidence in conducting real estate transactions online
Digital Text/Tool Selection and Integration: What Professors Teach

The DigiLit Framework suggests criteria for digital text and tool selection (content accuracy, intuitiveness, interactivity, quality) and integration (model a literacy skill or strategy, guide a literacy skill or strategy, model digital feature use, guide digital feature use) in literacy lessons. Using survey research, we explored which DigiLit criteria literacy professors prepared preservice teachers to use in K-12 settings. Participants included 199 literacy professors (194 from USA and one each from Australia, Canada, Caribbean, Middle East and Europe). We used a multivariate outcome logit/probit model to analyze how this was related to (a) professor characteristics, (b) institution characteristics, and (c) time. Findings showed that certain professor characteristics (e.g., interest in integrating technology, being knowledgeable about digital literacies), institution characteristics (e.g., access to equipment, professional development, technical support, incentives), and time to plan and practice integration were related to literacy professors&amp;rsquo; increased preparation of preservice teachers to use digital text or tool selection and integration. These findings provide specific ways to improve literacy teacher preparation by providing specific kinds of supports.
IoTeach:Learning Support System for IoT Programming by Integrating Real Devices and Sequential Contents

In recent years, Internet of Things(IoT) has become popular, the requirements for sharing the operation/mechanisms of IoT devices, such as Arduino and M5Stack are increasing. Moreover, owing to the coronavirus pandemic, many educational institutions have adopted online lectures, such as on-demand classes and online classes using video conference systems. For IoT programming education, these methods have challenges, such as a lack of linkage with real-world devices and source codes. In this study, we propose a system called “IoTeach”, which supports the learning of IoT programming by attaching a scripting language to sequential contents, such as videos and slides shared on the Web. The IoTeach can link videos and slides with real-world IoT devices and source codes. We describe the concept and implementation of the system in this study.
GUI Design Patterns for Improving the HCI in Explainable Artificial Intelligence

The rising number of artificial intelligence systems has increased the demand for transparency and accountability. This thesis addresses the human-computer interaction (HCI) challenges of explainable artificial intelligence (XAI), focusing on reusable solutions for transparency and fairness challenges. The main objectives we aim to address are 1) Identification of the most common and most significant HCI usability challenges in XAI; 2) Identification of good and reusable solutions for recognised HCI challenges in literature and existing XAI solutions, and 3) Their definition in the form of the design patterns. A catalogue of graphical user interface design patterns (i.e. reusable solutions to commonly occurring problems with a given context) in XAI is proposed. Expected benefits of the thesis include facilitating communication and collaboration between researchers and practitioners by providing shared vocabulary and proven design patterns, enhancing trust and accountability of AI solutions, promoting user-centred design and facilitating the evaluation and accelerating the maturation of XAI.
Fair and Robust Metric for Evaluating Touch-based Continuous Mobile Device Authentication

Mobile devices such as smartphones have an initial-login authentication to prevent third-party use. However, such authentication systems can be hacked by shoulder surfing, smudge attacks, and biometric duplication. Touch-based authentication is a continuous authentication method that can rapidly detect illegal use after an initial login. Various touch-based authentications have been proposed to date; however, evaluation experiments often lack assumptions regarding actual usage situations. To apply touch-based authentication for practical use, evaluation experiments should be conducted by constructing a realistic dataset. This study provides two guidelines for a fair and realistic evaluation of touch-based authentication.
Double-direction fisheye plane sweep stereo for road surface 3D reconstruction

This paper presents double-direction plane sweep stereo, an accurate 3D reconstruction method of the road surface using a fisheye stereo camera, where the normal vectors of the planes are rotated according to the camera pose. As a result, the proposed method can obtain a dense, accurate, and wide FoV reconstruction even in a situation where the road surface has a slope or steps. Moreover, a simple and fast approximation of the LR consistency check is presented. The consistency check efficiently removes outliers, especially the occluded region due to proximal obstacles. The experimental result of real image shows the effectiveness of the proposed method, where the reconstruction error of surface height is less than 2.5cm.
Application for Doctoral Consortium IUI 2023

invited-talk Share on Application for Doctoral Consortium IUI 2023 Author: Lukas Gunthermann University of Sussex, United Kingdom University of Sussex, United Kingdom 0000-0002-9297-179XView Profile Authors Info & Claims IUI '23 Companion: Companion Proceedings of the 28th International Conference on Intelligent User InterfacesMarch 2023Pages 233–236https://doi.org/10.1145/3581754.3584112Published:27 March 2023Publication History 0citation12DownloadsMetricsTotal Citations0Total Downloads12Last 12 Months12Last 6 weeks12 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Query semantic reconstruction for background in few-shot segmentation

Abstract Few-shot segmentation (FSS) aims to segment unseen classes using a few annotated samples. Typically, a prototype representing the foreground class is extracted from annotated support image(s) and is matched to features representing each pixel in the query image. However, models learnt in this way are insufficiently discriminatory, and often produce false positives: misclassifying background pixels as foreground. Some FSS methods try to address this issue by using the background in the support image(s) to help identify the background in the query image. However, the backgrounds of these images are often quite distinct, and hence, the support image background information is uninformative. This article proposes a method, QSR, that extracts the background from the query image itself, and as a result is better able to discriminate between foreground and background features in the query image. This is achieved by modifying the training process to associate prototypes with class labels including known classes from the training data and latent classes representing unknown background objects. This class information is then used to extract a background prototype from the query image. To successfully associate prototypes with class labels and extract a background prototype that is capable of predicting a mask for the background regions of the image, the machinery for extracting and using foreground prototypes is induced to become more discriminative between different classes. Experiments achieves state-of-the-art results for both 1-shot and 5-shot FSS on the PASCAL- $$5^{i}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>5</mml:mn> <mml:mi>i</mml:mi> </mml:msup> </mml:math> and COCO- $$20^{i}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>20</mml:mn> <mml:mi>i</mml:mi> </mml:msup> </mml:math> dataset. As QSR operates only during training, results are produced with no extra computational complexity during testing.
Development of a cerebral aneurysm segmentation method to prevent sentinel hemorrhage

Abstract Image segmentation being the first step is always crucial for brain aneurysm treatment planning; it is also crucial during the procedure. A robust brain aneurysm segmentation has the potential to prevent the blood leakage, also known as sentinel hemorrhage. Here, we present a method combining a multiresolution and a statistical approach in two dimensional domain to segment cerebral aneurysm in which the Contourlet transform (CT) extracts the image features, while the Hidden Markov Random Field with Expectation Maximization (HMRF-EM) segments the image, based on the spatial contextual constraints. The proposed algorithm is tested on Three-Dimensional Rotational Angiography (3DRA) datasets; the average values of segmentation accuracy, DSC, FPR, FNR, specificity, and sensitivity, are found to be 99.72%, 93.52%, 0.07%, 5.23%, 94.77%, and 99.96%, respectively.
An effective approach to improve the performance of eCPDP (early cross-project defect prediction) via data-transformation and parameter optimization

Cross-project defect prediction (CPDP) utilizes other finished projects (i.e., source project) data to predict defects of the current working project. Transfer learning (TL) has been mainly applied at CPDP to improve prediction performance by alleviating the data distribution discrepancy between different projects. However, existing TL-based CPDP techniques are not applicable at the unit testing phase since they require the entire historical target project data. As a result, they lose the chance to increase the product’s reliability in the early phase by applying the prediction results. The objective of the present study is to increase the product’s reliability in the early phase by proposing a novel TL-based CPDP technique applicable at the unit testing phase (i.e., eCPDP). We utilize singular value decomposition (SVD), which only requires source project data for TL. eCPDP performs similarly or better than the 8 state-of-the-art TL-based CPDP techniques on 9 different performance metrics over 24 projects. In conclusion, (1) we show that eCPDP is an applicable CPDP model at the unit testing phase. (2) It can help practitioners find and fix defects in an earlier phase than other TL-based CPDP techniques.
Span(Graph): a canonical feedback algebra of open transition systems

We show that $${\textbf{Span}}({\textbf{Graph}})_*$$ , an algebra for open transition systems introduced by Katis, Sabadini and Walters, satisfies a universal property. By itself, this is a justification of the canonicity of this model of concurrency. However, the universal property is itself of interest, being a formal demonstration of the relationship between feedback and state. Indeed, feedback categories, also originally proposed by Katis, Sabadini and Walters, are a weakening of traced monoidal categories, with various applications in computer science. A state bootstrapping technique, which has appeared in several different contexts, yields free such categories. We show that $${\textbf{Span}}({\textbf{Graph}})_*$$ arises in this way, being the free feedback category over $${\textbf{Span}}(\textbf{Set})$$ . Given that the latter can be seen as an algebra of predicates, the algebra of open transition systems thus arises—roughly speaking—as the result of bootstrapping state to that algebra. Finally, we generalize feedback categories endowing state spaces with extra structure: this extends the framework from mere transition systems to automata with initial and final states.
Neural Motion Planning for Autonomous Parking

This paper presents a hybrid motion planning strategy that combines a deep generative network with a conventional motion planning method. Existing planning methods such as A* and Hybrid A* are widely used in path planning tasks because of their ability to determine feasible paths even in complex environments; however, they have limitations in terms of efficiency. To overcome these limitations, a path planning algorithm based on a neural network, namely the neural Hybrid A*, is introduced. This paper proposes using a conditional variational autoencoder (CVAE) to guide the search algorithm by exploiting the ability of CVAE to learn information about the planning space given the information of the parking environment. An efficient expansion strategy is utilized based on a distribution of feasible trajectories learned in the demonstrations. The proposed method effectively learns the representations of a given state, and shows improvement in terms of computational time and the number of node expanded related to algorithm performance.
A generalisation of the Phase Kick-Back

Abstract In this paper, we present a generalisation of the Phase Kick-Back technique, which is central to some of the classical algorithms in quantum computing. We will begin by recalling the Phase Kick-Back technique to then introduce the new generalised version for $$f:\{0,1\}^{n}\rightarrow \{0,1\}^{m}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>f</mml:mi> <mml:mo>:</mml:mo> <mml:msup> <mml:mrow> <mml:mo>{</mml:mo> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo>}</mml:mo> </mml:mrow> <mml:mi>n</mml:mi> </mml:msup> <mml:mo>→</mml:mo> <mml:msup> <mml:mrow> <mml:mo>{</mml:mo> <mml:mn>0</mml:mn> <mml:mo>,</mml:mo> <mml:mn>1</mml:mn> <mml:mo>}</mml:mo> </mml:mrow> <mml:mi>m</mml:mi> </mml:msup> </mml:mrow> </mml:math> functions using the eigenvalues of the oracle function $$\textbf{U}_f$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>U</mml:mi> <mml:mi>f</mml:mi> </mml:msub> </mml:math> . After that, we will present a new generalised version of the Deutsch–Jozsa problem and how it can be solved using the previously defined technique. We will also deal with a generalised version of the Bernstein–Vazirani problem and solve it using the generalised Phase Kick-Back. Finally, we show how we can use this technique to obtain an algorithm for Simon’s problem that improves the classical one.
Transport properties in directed quantum walks on the line

We obtained analytical expressions considering a directed continuous-time quantum walk on a directed infinite line using Bessel functions, expanding previous results in the literature, for a general initial condition. We derive the equation for the probability distribution and show how to recover normal and enhanced decay rates for the survival probability by adjusting the phase factor of the direction of the graph. Our result shows that the mean and standard deviation for a specific non-local initial condition do not depend on the direction.
Gaussian class-conditional simplex loss for accurate, adversarially robust deep classifier training

Abstract In this work, we present the Gaussian Class-Conditional Simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that improves over the state-of-the-art in terms of classification accuracy and adversarial robustness, with little extra cost for network training. The proposed method learns a mapping of the input classes onto Gaussian target distributions in a latent space such that a hyperplane can be used as the optimal decision surface. Instead of maximizing the likelihood of target labels for individual samples, our loss function pushes the network to produce feature distributions yielding high inter-class separation and low intra-class separation. The mean values of the learned distributions are centered on the vertices of a simplex such that each class is at the same distance from every other class. We show that the regularization of the latent space based on our approach yields excellent classification accuracy. Moreover, GCCS provides improved robustness against adversarial perturbations, outperforming models trained with conventional adversarial training (AT). In particular, our model learns a decision space that minimizes the presence of short paths toward neighboring decision regions. We provide a comprehensive empirical evaluation that shows how GCCS outperforms state-of-the-art approaches over challenging datasets for targeted and untargeted gradient-based, as well as gradient-free adversarial attacks, both in terms of classification accuracy and adversarial robustness.
Unsupervised Ensemble Learning Improves Discriminability of Stochastic Neighbor Embedding

Abstract The purpose of feature learning is to obtain effective representation of the raw data and then improve the performance of machine learning algorithms such as clustering or classification. Some of the existing feature learning algorithms use discriminant information in the data to improve the representation of data features, but the discrimination of the data feature representation is not enough. In order to further enhance the discrimination, discriminant feature learning based on t-distribution stochastic neighbor embedding guided by pairwise constraints (pcDTSNE) is proposed in this paper. pcDTSNE introduces pairwise constraints by clustering ensemble and uses these pairwise constraints to impose penalties on the objective function, which makes sample points in the mapping space present stronger discrimination. In order to verify the feature learning performance of pcDTSNE, extensive experiments are carried out on several public data sets. The experimental results show that the expression ability of data representation generated by pcDTSNE is further improved.
Evaluation of the Explanatory Power Of Layer-wise Relevance Propagation using Adversarial Examples

Abstract Approaches for visualizing and explaining the decision process of convolutional neural networks (CNNs) have recently received increasing attention. Particularly popular approaches are so-called saliency methods, which aim to assign a valence to each input pixel based on its importance and influence on the classification via saliency maps. In our paper, we contribute by a novel analyzing approach build on adversarial examples to investigate the explanatory power of saliency methods exemplified by layer-wise relevance propagation (LRP). Based on the hypothesis that distinct decisions, such as an image’s classification and the classification of its corresponding adversarial examples, should yield to dissimilar saliency maps to provide transparent rationales, we break down relevance scores of images and corresponding adversarial examples and analyze them using a comprehensive statistical evaluation. It turns out that different relevance decomposition rules of LRP do not lead to clearly distinguishable saliency maps for images and corresponding adversarial examples, neither in terms of their contour lines, nor in terms of the statistical analysis.
Rethinking the Value of Local Feature Fusion in Convolutional Neural Networks

Traditional CNN head for classification tasks typically consists of a global average pooling layer before the last fully-connected classifier. However, such a simple and light-weighted head lacks the ability of feature fusion, and can’t give full play to the strong feature extraction ability of the network body. In the present work, we analyze the Basic Block and Bottleneck structure in ResNet in-depth and reveal the importance of performing feature fusion inside local patches via $$1\times 1$$ convolution. We propose a new head structure consisting of three stages with a series of $$1\times 1$$ convolution to replace global average pooling. With little additional FLOPs and inference speed drop, our new head improves the accuracy for ResNet18 by 3.6% on ImageNet, and for ResNet56 by 5.0% on CIFAR-100.
Improvement of Association Algorithm Based on Matrix Optimization

In order to solve the problems of high memory usage and low efficiency of frequent itemsets generation in the process of data mining, the existing association algorithm is improved. The data information is expressed in the form of a Boolean matrix, and the matrix is continuously simplified in the operation to optimize the item set connection method, thereby reducing memory consumption and improving algorithm efficiency.