Reviewerly/exHarmony huggingface dataset:
Media Architecture in Architecture Studio Education capturing dynamics in the process

Virtual design environment tools such as virtual reality can refine the methods of existing Media Architecture design processes. Architecture has the potential to become dynamic and adapt rather than providing static characteristics. Media Architecture can be understood as materials or objects with dynamic properties, such as interactive sources of light or moving elements, which embody the physical space on an architectonic scale. In the higher education context virtual design environment tools are able to refine traditional teaching approaches while discovering new ways of design thinking and creating design solutions in architecture schools. These visual communication technology tools allow architects to engage more effectively with stakeholders to create a connection that goes beyond the planning process. This research focuses on the use of immersive technologies in the design of Media Architecture in architecture schools in Australia and Germany. Based on a literature review of architectural education, Media Architecture, and virtual design environment tools a recommendation for a design unit was proposed, implemented and evaluated in an architectural design studio setting. The most common approach in architectural education, how to learn to design, is a project-based approach called "design studio". In a "design studio", future architects explore ideas and concepts, create and evaluate alternatives, and decide and act. This research emphases on architecture students' learning experience, who have been exposed to the emerging discipline of Media Architecture and the state-of-the-art technology approach to design Media Architecture with virtual reality. We conducted three case studies with advanced undergraduate architecture students in their third year and masters in Australia and Germany. Within these case studies, we developed educational content for designing Media Architecture while using virtual design environment tools in a higher education context. In order to motivate and evaluate students experiences in these scenarios Keller's motivation design model ARCS (Attention, Relevance, Confidence, Satisfaction) was utilized in the design studio context to design Media Architecture with virtual design environment tools. A response of the students to the new studio content and approaches in the educational environment was evaluated through surveys, interviews, and observations. We are in progress of reviewing the collected data at this stage. The results of the case studies will allow us to explore educational Media Architecture approaches further, such as simplifying content delivery and eliminating issues. The outcome of this study are preliminary recommendations for teaching architecture students the use of virtual design environment tools in the design process of Media Architecture.
A Comparative Analysis of Machine and Deep Learning Models for Cervical Cancer Classification

Machine learning and deep learning algorithms have made significant contributions to healthcare industry. These algorithms aid in improving the accuracy and reliability of diagnosis of various diseases. Moreover they also assist medical practitioners in prognosis thus reducing the time and complexity of work. But on the other hand we also have certain diseases for which there is very less awareness among people. One such deadly disease is cervical cancer. It is a life threatening disease but it can be prevented by early prognosis and treatment. However most of the countries have no effective screening techniques for this type of cancer. In this study we incorporate various deep learning and machine learning models for classification of normal and cancerous cervical cells as well as their types and a comparative analysis of performance measures such as accuracy, sensitivity, specificity and F1 score is made.
HINFShot: A Challenge Dataset for Few-Shot Node Classification in Heterogeneous Information Network

Few-shot learning aims to generalize to novel classes. It has achieved great success in image and text classification tasks. Inspired by such success, few-shot node classification in homogeneous graph has attracted much attention but few works have begun to study this problem in Heterogeneous Information Network (HIN) so far. We consider few-shot learning in HIN and study a pioneering problem HIN Few-Shot Node Classification (HIN-FSNC) that aims to generalize the node types with sufficient labeled samples to unseen node types with only few-labeled samples. However, existing HIN datasets contain just one labeled node type, which means they cannot meet the setting of unseen node types. To facilitate the investigation of HIN-FSNC, we propose a large-scale academic HIN dataset called HINFShot. It contains 1,235,031 nodes with four node types (author, paper, venue, institution) and all the nodes regardless of node type are divided into 80 classes. Finally, we conduct extensive experiments on HINFShot and the result indicates a significant challenge of identifying novel classes of unseen node types in HIN-FSNC.
Augmented Reed-Muller Codes of High Rate and Erasure Repair

We present two families of augmented Reed-Muller (ARM) codes, which are evaluation codes obtained by adding specific vectors to a Reed-Muller code. We develop exact repair schemes for single erasures for these ARM codes. When a dimension and a base field are fixed, we give examples where ARM codes provide a lower bandwidth in comparison with Reed-Solomon codes. We analyze the asymptotical behavior when ARM codes achieve the maximum rate.
TQEA: Temporal Quantum Error Analysis

The growth of need of quantum computers in many domains such as machine learning, numerical scientific simulation and finance has urged the quantum computers to produce more stable and less error-prone results. However, to mitigate the impact of the noise inside each quantum device remains a present challenge. In this paper, we investigate the temporal behavior of noisy intermediate-scale quantum (NISQ) computer errors based on calibration data and the characteristics of individual devices. In particular, we collect calibration data of IBM-Q machines over 90 days and compare the quantum error robustness across processor types, quantum topology and quantum volumes. We analyze the aging effect by comparing the quantum error data from four IBM quantum computers during 2019-2021 showing that only one computer experienced significant error growth overtime. In this study, we simply analyze the collected data and run temporal test analysis to observe the behaviour of IBM-Q computers.
Ensemble Method for Credit Card Fraud Detection

Traditional credit card fraud detection methods are based on expert experience, while autonomous and intelligent fraud detection has become a direction of study and application. The main problems in fraud detection are class imbalance, concept drift and verification latency. Many works have been proposed to solve class imbalance and concept drift, but few of them consider verification latency. In this paper, we focus on this problem and propose an autonomous and intelligent new method called FD for credit card fraud detection. First, we propose a noise-robust boosting method called SCBoost. Second, we propose a method called k-SDR based on the idea of clustering, it regards a small amount of labeled data as cluster center and conducts clustering according to the distance ratio. Finally, our FD integrates SCBoost and k-SDR. Experiments are conducted to verify the effectiveness of SCBoost, k-SDR and FD.
SimYA: A virtual reality–based construction studio simulator

In architectural education, generally experience is the most permanent way to learn professional skills. However, perhaps as a result of modern education, it is not possible to learn everything about the building by experience. SimYA project aims to help the development of a new generation construction studio based on learning by doing and experiencing in architectural education by using virtual reality (VR) technologies. Within the scope of the project, an interactive VR-based computer simulation (SimYA) developed to demonstrate basic construction elements such as foundations, walls, and roofs. The effectiveness of the SimYA against the traditional method has been tested with a scientific experiment. A total of 32 volunteer architecture students participated in the experiment. The participants were divided into two equal groups within the scope of the experiment. These groups were named as SimYA and control group in a way to be compatible with their related method. First, a pre-test was applied in order to evaluate the current knowledge of the groups. After that, the first group was taught with the VR supported SimYA program and the second group was taught with the traditional method. According to test results, the success rate of the SimYA group increased from 5.63% to 74.86%, and the success rate of the control group increased from 3.12% to 57.95%. These results indicate that the SimYA project has achieved the targeted success. It is thought that placing the building elements and materials in the virtual environment with student’s own hands and receiving visual and audio information from the interactive building elements are an important factor in this success.
The architectural divergence problem in security and privacy of eHealth IoT product lines

The Internet of Things (IoT) seamlessly becomes integrated into many aspects of daily life, and in the case of healthcare, it arises in the shape of eHealth IoT systems. Evidently, the design of such systems must apply best practices when it comes to security and privacy, in addition to ensuring compliance with various national and international regulations. When it comes to the required functionality, commonalities and variations can effectively be managed in a product line approach that involves deriving specific application architecture variants from a common reference architecture.
Real-time Privacy Preserving Framework for Covid-19 Contact Tracing

The recent unprecedented threat from COVID-19 and past epidemics, such as SARS, AIDS, and Ebola, has affected millions of people in multiple countries. Countries have shut their borders, and their nationals have been advised to self-quarantine. The variety of responses to the pandemic has given rise to data privacy concerns. Infection prevention and control strategies as well as disease control measures, especially real-time contact tracing for COVID-19, require the identification of people exposed to COVID-19. Such tracing frameworks use mobile apps and geolocations to trace individuals. However, while the motive may be well intended, the limitations and security issues associated with using such a technology are a serious cause of concern. There are growing concerns regarding the privacy of an individual's location and personal identifiable information (PII) being shared with governments and/or health agencies. This study presents a real-time, trust-based contact-tracing framework that operates without the use of an individual's PII, location sensing, or gathering GPS logs. The focus of the proposed contact tracing framework is to ensure real-time privacy using the Bluetooth range of individuals to determine others within the range. The research validates the trust-based framework using Bluetooth as practical and privacy-aware. Using our proposed methodology, personal information, health logs, and location data will be secure and not abused. This research analyzes 100,000 tracing dataset records from 150 mobile devices to identify infected users and active users.
ANN and AdaBoost Based Effective Face Recognition and Improvement of Classification Training

In this paper to design a modern technique of face reorganization. Face reorganization is approach to identification of human face by using artificial intelligence. Now proposed algorithm to implementation of MATLAB 2015a software using Image processing toolbox. In this paper enhancement of image quality and reorganization rate using ANN and Adaboost algorithm. This paper result and simulation section mentioned improvement result as compare to previous research articles.
Enhanced AlexNet with Super-Resolution for Low-Resolution Face Recognition

With the advancement in deep learning, high-resolution face recognition has achieved outstanding performance that makes it widely adopted in many real-world applications. Face recognition plays a vital role in visual surveillance systems. However, the images captured by the security cameras are at low resolution causing the performance of the low-resolution face recognition relatively inferior. In view of this, we propose an enhanced AlexNet with Super-Resolution and Data Augmentation (SRDA-AlexNet) for low-resolution face recognition. Firstly, image super-resolution improves the quality of the low-resolution images to high-resolution images. Subsequently, data augmentation is applied to generate variations of the images for larger data size. An enhanced AlexNet with batch normalization and dropout regularization is then used for feature extraction. The batch normalization aims to reduce the internal covariate shift by normalizing the input distributions of the mini-batches. Apart from that, the dropout regularization improves the generalization capability and alleviates the overfitting of the model. The extracted features are then classified using k-Nearest Neighbors method for low-resolution face recognition. Empirical results demonstrate that the proposed SRDA-AlexNet outshines the methods in comparison.
Learning Adaptive Embedding Considering Incremental Class

Class-Incremental Learning (CIL) aims to train a reliable model with the streaming data, which emerges unknown classes sequentially. Different from traditional closed set learning, CIL has two main challenges: (1) Novel class detection. The initial training data only contains incomplete classes, and streaming test data will accept unknown classes. Therefore, the model needs to not only accurately classify known classes, but also effectively detect unknown classes; (2) Model expansion. After the novel classes are detected, the model needs to be updated without re-training using the entire previous data. However, traditional CIL methods have not fully considered these two challenges. To this end, we propose a Class-Incremental Learning without Forgetting (CILF) framework. In detail, CILF designs to regularize classification with decoupled prototype based loss, which can improve the intra-class and inter-class structure significantly, and acquire a compact embedding representation for novel class detection in result. Then, CILF employs a learnable curriculum clustering operator to estimate the number of semantic clusters via fine-tuning the learned network, in which curriculum operator can adaptively learn the embedding in self-taught form. Last, with the labeled streaming test data, CILF can update the network with robust regularization to mitigate the catastrophic forgetting.
Towards Better Recognition of Age, Gender, and Number of Viewers in a Smart TV Environment

The retrieval of age and gender-related information from digital facial images has recently gained attention. This information is used for numerous purposes, including better Human-Computer Interaction (HCI), security, privacy, and recommendations on smartphones and computers. However, little attention has been given to utilize this information for emerging computing devices, i.e., smart TV. The detection of gender, age, and the number of viewers in smart TV watching environments is challenging due to several factors, such as watching distance and room lighting conditions. In this research, efforts have been made to develop a novel approach to accurately recognize numbers, genders, and age groups of Smart TV viewers based on human faces. A custom dataset of facial images representing the Smart TV room environment is developed and used to train our proposed model. Our proposed architecture model is based on Convolutional Neural Network (CNN). We found 91.1% accuracy of training for the gender classification and 79.1% for predicting age, specifically in a smart TV watching environment where watching distance and room lighting condition matters.
A Denoising Convolutional Neural Network for Self-Supervised Rank Effectiveness Estimation on Image Retrieval

Image and multimedia retrieval has established as a prominent task in an increasingly digital and visual world. Mainly supported by decades of development on hand-crafted features and the success of deep learning techniques, various different feature extraction and retrieval approaches are currently available. However, the frequent requirements for large training sets still remain as a fundamental bottleneck, especially in real-world and large-scale scenarios. In the scarcity or absence of labeled data, choosing what retrieval approach to use became a central challenge. A promising strategy consists in to estimate the effectiveness of ranked lists without requiring any groundtruth data. Most of the existing measures exploit statistical analysis of the ranked lists and measure the reciprocity among lists of images in the top positions. This work innovates by proposing a new and self-supervised method for this task, the Deep Rank Noise Estimator (DRNE). An algorithm is presented for generating synthetic ranked list data, which is modeled as images and provided for training a Convolutional Neural Network that we propose for effectiveness estimation. The proposed model is a variant of the DnCNN (Denoiser CNN), which intends to interpret the incorrectness of a ranked list as noise, which is learned by the network. Our approach was evaluated on 5 public image datasets and different tasks, including general image retrieval and person re-ID. We also exploited and evaluated the complementary between the proposed approach and related rank-based approaches through fusion strategies. The experimental results showed that the proposed method is capable of achieving up to 0.88 of Pearson correlation with MAP measure in general retrieval scenarios and 0.74 in person re-ID scenarios.
Structure from Motion and Mosaicking for High-Throughput Field-Scale Phenotyping

This work presents a 3D-enabled method to register aerial image sequences. Our approach is based on a novel Bootstrapped Structure-from-Motion (BSfM)Bootstrapped Structure-from-Motion (BSfM) followed by analytical homography reprojection or georegistration. BSfMBootstrapped Structure-from-Motion (BSfM) is a fast and robust method to recover the 3D exterior orientation of the camera poses and the scene 3D structure from image sequences. The recovered 3D parameters are used in an analytical approach to estimate homography matrices that project the input images onto the dominant ground plane in the scene to produce a global mosaic of the field and plants. Preliminary experimental results validate the approach and show satisfactory results suitable for scaling up to support high-throughput field phenotyping (HTP) for agricultural crop field experiments.
Learning to See Through Obstructions With Layered Decomposition

We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions, or adherent raindrops, from a short sequence of images captured by a moving camera. Our method leverages motion differences between the background and obstructing elements to recover both layers. Specifically, we alternate between estimating dense optical flow fields of the two layers and reconstructing each layer from the flow-warped images via a deep convolutional neural network. This learning-based layer reconstruction module facilitates accommodating potential errors in the flow estimation and brittle assumptions, such as brightness consistency. We show that the proposed approach learned from synthetically generated data performs well to real images. Experimental results on numerous challenging scenarios of reflection and fence removal demonstrate the effectiveness of the proposed method.
A Large-Scale Investigation of Local Variable Names in Java Programs: Is Longer Name Better for Broader Scope Variable?

Variables are fundamental elements of software, and their names hold vital clues to comprehending the source code. It is ideal that a variable’s name should be informative that anyone quickly understands its role. When a variable’s scope gets broader, the demand for such an informative name becomes higher. Although the standard naming conventions provide valuable guidelines for naming variables, there is a lack of concrete and quantitative criteria regarding a better name. That challenge in naming variables is the motivation of the quantitative investigation conducted in this paper. The investigation collects 637,077 local variables from 1,000 open-source Java projects to get a detailed view of the variable naming trend. The data analysis reveals frequently-used terms for variable names, the naming styles, and the length of names when the variable scopes are broad. The results showed that developers prefer to use fully spelled English words or compounded names for broad-scope variables, but they tend to avoid long names; Developers often use simple words or abbreviations shorter than seven or eight characters.
Improved Private and Secure Distributed Matrix Multiplication

Consider the problem of a user having a private matrix <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> non-colluding servers sharing a library of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L (L &gt; \ 1)$</tex> matrices <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$B^{(0)}, B^{(1)},\ldots, B^{(L-1)}$</tex> , for which the user wishes to compute <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$AB^{(\theta)}$</tex> for some <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\theta\in[0, L)$</tex> without revealing any information of the matrix <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A$</tex> to the servers, and keeping the index <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\theta$</tex> private to the servers. This problem is known as private and secure distributed matrix multiplication (PSDMM) and is supposed to have wide application potential. However, studies of PSDMM are still scarce in the literature. In this paper, we propose a new efficient private and secure distributed matrix multiplication coding scheme, which has a better performance than state-of-the-art schemes in that it achieves a smaller recovery threshold and download cost as well as providing a more flexible tradeoff between the upload and download costs.
Border Detection for Seamless Connection of Historical Cadastral Maps

This paper presents a set of methods for detection of important features in historical cadastral maps. The goal is to allow a seamless connection of the maps based on such features. The connection is very important so that the maps can be presented online and utilized easily. To the best of our knowledge, this is the first attempt to solve this task fully automatically. Compared to the manual annotation which is very time-consuming we can significantly reduce the costs and provide comparable or even better results.