Reviewerly/exHarmony huggingface dataset:
CS$$^2$$: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human Intervention

The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance. To address such a problem of data and label scarcity, generative models have been developed to augment the training datasets. Previously proposed generative models usually require manually adjusted annotations (e.g., segmentation masks) or need pre-labeling. However, studies have found that these pre-labeling based methods can induce hallucinating artifacts, which might mislead the downstream clinical tasks, while manual adjustment could be onerous and subjective. To avoid manual adjustment and pre-labeling, we propose a novel controllable and simultaneous synthesizer (dubbed CS $$^2$$ ) in this study to generate both realistic images and corresponding annotations at the same time. Our CS $$^2$$ model is trained and validated using high resolution CT (HRCT) data collected from COVID-19 patients to realize an efficient infections segmentation with minimal human intervention. Our contributions include 1) a conditional image synthesis network that receives both style information from reference CT images and structural information from unsupervised segmentation masks, and 2) a corresponding segmentation mask synthesis network to automatically segment these synthesized images simultaneously. Our experimental studies on HRCT scans collected from COVID-19 patients demonstrate that our CS $$^2$$ model can lead to realistic synthesized datasets and promising segmentation results of COVID infections compared to the state-of-the-art nnUNet trained and fine-tuned in a fully supervised manner.
Making space for faith, religion, and spirituality in prosocial HCI

research-article Share on Making space for faith, religion, and spirituality in prosocial HCI Authors: Khushnood Naqshbandi University of Sydney University of SydneySearch about this author , Kristina Mah University of Sydney University of SydneySearch about this author , Naseem Ahmadpour University of Sydney University of SydneyView Profile Authors Info & Claims InteractionsVolume 29Issue 4July - August 2022 pp 62–67https://doi.org/10.1145/3544301Online:29 June 2022Publication History 2citation463DownloadsMetricsTotal Citations2Total Downloads463Last 12 Months463Last 6 weeks101 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
User Experience Guidelines of Augmented Reality Application for Historical Tourism

Malaysia Smart Tourism 4.0 is a government initiative that aims to transform the tourism industry to the next level. Tourism 4.0 is the next generation of the tourism industry that uses the latest industrial revolution technologies such as Augmented Reality (AR). AR is a new technology that uses computer graphics to create the illusion of being in the real world. In recent years, the Mobile Augmented Reality (MAR) approach has been used in many sectors such as tourism, education, and healthcare. User experience is crucial for MAR applications since it attempts to meet the user's needs and enhance engagement. However, limited studies on user experience in historical tourism have been explored due to the lack of evidence and empirical research. Thus, this study is to propose user experience guidelines for an Augmented Reality application for historical tourism. More than 60 articles related to AR in tourism were analyzed using thematic analysis in this study. The study resulted in two findings: (1) the user experience guidelines of Augmented Reality for historical tourism applications, and (2) user experience that was influenced by the application features and the requirements of tourists. The findings will be used to propose an UX design for MAR applications in the context of historical tourism. Most significantly, the number of successful deployment use cases for MAR applications is currently limited, especially in the tourism industry. Thus, it is critical to have a comprehensive set of variables has been considered for success.
Defining Teamwork Productivity Factors in Agile Software Development

Teamwork productivity plays a substantial role in attaining successful projects in agile software development. For improving agile software development, it is necessary to look at many factors that influence agile teamwork productivity. Thus, there is a need to identify these influential ones among factors. Identifying these influential factors affecting agile teamwork productivity can enable the teams to pertain to where they need to enforce the elbow grease to improve productivity. Teams in software organizations will improve their productivity by considering these teamwork factors in agile software development. In this respect, the classification of the teamwork factors that might cause an influence on the productivity of the agile software development teams becomes the indication of divergence for the choice and characterization of enhancement approaches. We carried on a systematic literature review to execute such analysis in which we included 53 primary studies. The systematic literature review aimed to identify and classify the factors influencing teamwork productivity in agile software development. As a result of the systematic literature review, we identified 77 influential factors and classified these factors into technical, non-technical, organizational, environmental, project management, and user requirements level factors that affect teamwork productivity in agile software development. Based on this data, software organizations can mend the teamwork productivity of their teams by appraising the impact factors that best fit their context.
Gram stain

Gram staining is a differential staining technique performed when a bacterial infection is suspected. The technique classifies bacteria into two broad groups: Gram negative and gram positive bacteria. In this net educational video, we have demonstrated the method of Gram staining.
新学習指導要領に準拠した小学校・中学校の教科書における電気に関する記述内容の調査

Electricity, which is related with many subjects, is a useful theme for STEM/STEAM education. Therefore, it is important for teachers to recognize the relation. On the other hand, in the Elementary and Secondary Education Support Working Group of the Institute of Electrical Engineers of Japan Social Cooperation Committee investigated the relation of the Course of Study to the booklets “Fundamental Understanding of Electricity and Electric Energy Series” prepared by IEEJ in order to spread to schools. However, the mapping table showing the relation was for only Science. In addition, there have been no cases investigated regarding the context in which electricity was described on the textbook. So, the purpose of this study was to investigate the contents regarding electricity in the newly used textbooks based on the new Course of Study (2017 revision). The Subjects researched were Technology, Home Economics, Science, Social Studies, Art and Handcraft, and Living Environment Studies.
Image encryption based on a novel memristive chaotic system, Grain-128a algorithm and dynamic pixel masking

In this paper, we first propose a memristive chaotic system and implement it by circuit simulation.The chaotic dynamics and various attractors are analysed by using phase portrait, bifurcation diagram, and Lyapunov exponents.In particular, the system has robust chaos in a wide parameter range and the initial value space, which is favourable to the security communication application.Consequently, we further explore its application in image encryption and present a new scheme.Before image processing, the external key is protected by the Grain-128a algorithm and the initial values of the memristive system are updated with the plain image.We not only perform random pixel extraction and masking with the chaotic cipher, but also use them as control parameters for Brownian motion to obtain the permutation matrix.In addition, multiplication on the finite field GF(2 8) is added to further enhance the cryptography.Finally, the simulation results verify that the proposed image encryption scheme has better performance and higher security, which can effectively resist various attacks.
Machine-Learning based Decoding of Surface Code Syndromes in Quantum Error Correction

Errors in surface code have typically been decoded by Minimum Weight Perfect Matching (MWPM) based method.Recently, neural-network-based Machine Learning (ML) techniques have been employed for this purpose, although how an ML decoder will behave in a more realistic asymmetric noise model has not been studied.In this article we (i) establish a methodology to formulate the surface code decoding problem as an ML classification problem, and (ii) propose a two-level (low and high) ML-based decoding scheme, where the first (low) level corrects errors on physical qubits and the second (high) level corrects any existing logical errors, for various noise models.Our results show that our proposed decoding method achieves ∼ 10× and ∼ 2× higher values of pseudo-threshold and threshold respectively, than for those with MWPM.We also empirically establish that usage of more sophisticated ML models with higher training/testing time, do not provide significant improvement in the decoder performance.
Resource Recommendation Based on Industrial Knowledge Graph in Low-Resource Conditions

Abstract Resource recommendation is extremely challenging under low-resource conditions because representation learning models require sufficient triplets for their training, and the presence of massive long-tail resources leads to data sparsity and cold-start problems. In this paper, an industrial knowledge graph is developed to integrate resources for manufacturing enterprises, and we further formulate long-tail recommendations as a few-shot relational learning problem of learning-to-recommend resources with few interactions under low-resource conditions. First, an industrial knowledge graph is constructed based on the predesigned resource schema. Second, we conduct schema-based reasoning on the schema to heuristically complete the knowledge graph. At last, we propose a multi-head attention-based meta relational learning model with schema-based reasoning to recommend long-tail resources under low-resource conditions. With the IN-Train setting, 5-shot experimental results on the NELL-One and Wiki-One datasets achieve average improvements of 28.8 and 13.3% respectively, compared with MetaR. Empirically, the attention mechanism with relation space translation learns the most important relations for fast convergence. The proposed graph-based platform specifies how to recommend resources using the industrial knowledge graph under low-resource conditions.
An Edge Aware Motion Modeling Technique Leveraging on the Discrete Cosine Basis Oriented Motion Model and Frame Super Resolution

To capture motion homogeneity between successive frames, the edge position difference (EPD) measure based motion modeling (EPD-MM) has shown good motion compensation capabilities. The EPD-MM technique is underpinned by the fact that from one frame to next, edges map to edges and such mapping can be captured by an appropriate motion model. An example of such a motion model is the discrete cosine basis oriented (DCO) motion model, which can capture complex motion and has a smooth and sparse representation. However, for higher resolution video sequences, the baseline EPD-MM approach equipped with the DCO motion model, may fail to approximate the underlying motion field accurately. This is due to the difficulty in fitting motion model parameters by incorporating significantly large number of moving edge pixels. Observing the fact that in lower resolution version of the current frame <tex>$C$</tex>, the same scene structure is present although scaled down moving objects contain smaller number of edge pixels; in this paper we propose to carry out the EPD-MM technique, augmented by the DCO motion model, over lower resolution form of <tex>$C$</tex>. The resultant edge motion compensated prediction is then upsampled back to the original resolution of <tex>$C$</tex>, employing single image super resolution (SISR) technique. Experimental results show an improved prediction PSNR of 1.85 dB, on average, from the proposed approach compared to that of the baseline EPD-MM. Moreover, if this predicted frame is employed as an additional reference frame to encode <tex>$C$</tex>, bit rate savings of up to 7.90&#x0025; is achievable over a HEVC reference.
Perceptual Quality Assessment of Omnidirectional Images

Omnidirectional images, also called 360◦images, have attracted extensive attention in recent years, due to the rapid development of virtual reality (VR) technologies. During omnidirectional image processing including capture, transmission, consumption, and so on, measuring the perceptual quality of omnidirectional images is highly desired, since it plays a great role in guaranteeing the immersive quality of experience (IQoE). In this paper, we conduct a comprehensive study on the perceptual quality of omnidirectional images from both subjective and objective perspectives. Specifically, we construct the largest so far subjective omnidirectional image quality database, where we consider several key influential elements, i.e., realistic non-uniform distortion, viewing condition, and viewing behavior, from the user view. In addition to subjective quality scores, we also record head and eye movement data. Besides, we make the first attempt by using the proposed database to train a convolutional neural network (CNN) for blind omnidirectional image quality assessment. To be consistent with the human viewing behavior in the VR device, we extract viewports from each omnidirectional image and incorporate the user viewing conditions naturally in the proposed model. The proposed model is composed of two parts, including a multi-scale CNN-based feature extraction module and a perceptual quality prediction module. The feature extraction module is used to incorporate the multi-scale features, and the perceptual quality prediction module is designed to regress them to perceived quality scores. The experimental results on our database verify that the proposed model achieves the competing performance compared with the state-of-the-art methods.
Leveraging Acoustic Contextual Representation by Audio-textual Cross-modal Learning for Conversational ASR

Leveraging context information is an intuitive idea to improve performance on conversational automatic speech recognition(ASR). Previous works usually adopt recognized hypotheses of historical utterances as preceding context, which may bias the current recognized hypothesis due to the inevitable historicalrecognition errors. To avoid this problem, we propose an audio-textual cross-modal representation extractor to learn contextual representations directly from preceding speech. Specifically, it consists of two modal-related encoders, extracting high-level latent features from speech and the corresponding text, and a cross-modal encoder, which aims to learn the correlation between speech and text. We randomly mask some input tokens and input sequences of each modality. Then a token-missing or modal-missing prediction with a modal-level CTC loss on the cross-modal encoder is performed. Thus, the model captures not only the bi-directional context dependencies in a specific modality but also relationships between different modalities. Then, during the training of the conversational ASR system, the extractor will be frozen to extract the textual representation of preceding speech, while such representation is used as context fed to the ASR decoder through attention mechanism. The effectiveness of the proposed approach is validated on several Mandarin conversation corpora and the highest character error rate (CER) reduction up to 16% is achieved on the MagicData dataset.
Aspect Feature Distillation and Enhancement Network for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task designed to identify the polarity of a target aspect. Some works introduce various attention mechanisms to fully mine the relevant context words of different aspects, and use the traditional cross-entropy loss to fine-tune the models for the ABSA task. However, the attention mechanism paying partial attention to aspect-unrelated words inevitably introduces irrelevant noise. Moreover, the cross-entropy loss lacks discriminative learning of features, which makes it difficult to exploit the implicit information of intra-class compactness and inter-class separability. To overcome these challenges, we propose an Aspect Feature Distillation and Enhancement Network (AFDEN) for the ABSA task. We first propose a dual-feature extraction module to extract aspect-related and aspect-unrelated features through the attention mechanisms and graph convolutional networks. Then, to eliminate the interference of aspect-unrelated words, we design a novel aspect-feature distillation module containing a gradient reverse layer that learns aspect-unrelated contextual features through adversarial training, and an aspect-specific orthogonal projection layer to further project aspect-related features into the orthogonal space of aspect-unrelated features. Finally, we propose an aspect-feature enhancement module that leverages supervised contrastive learning to capture the implicit information between the same sentiment labels and between different sentiment labels. Experimental results on three public datasets demonstrate that our AFDEN model achieves state-of-the-art performance and verify the effectiveness and robustness of our model.
CTnoCVR

In recent years, multi-task learning models based on deep learning in recommender systems have attracted increasing attention from researchers in industry and academia. Accurately estimating post-click conversion rate (CVR) is often considered as the primary task of multi-task learning in recommender systems. However, some advertisers may try to get higher click-through rates (CTR) by over-decorating their ads, which may result in excessive exposure to samples with lower CVR. For example, some only eye-catching clickbait have higher CTR, but actually, CVR is very low. As a result, the overall performance of the recommender system will be hurt. In this paper, we introduce a novelty auxiliary task called CTnoCVR, which aims to predict the probability of events with click but no-conversion, in various state-of-the-art multi-task models of recommender systems to promote samples with high CVR but low CTR. Plentiful Experiments on a large-scale dataset gathered from traffic logs of Taobao's recommender system demonstrate that the introduction of CTnoCVR task significantly improves the prediction effect of CVR under various multi-task frameworks. In addition, we conduct the online test and evaluate the effectiveness of our proposed method to make those samples with high CVR and low CTR rank higher.
RepMix: Representation Mixing for Robust Attribution of Synthesized Images

Rapid advances in Generative Adversarial Networks (GANs) raise new challenges for image attribution; detecting whether an image is synthetic and, if so, determining which GAN architecture created it. Uniquely, we present a solution to this task capable of 1) matching images invariant to their semantic content; 2) robust to benign transformations (changes in quality, resolution, shape, etc.) commonly encountered as images are re-shared online. In order to formalize our research, a challenging benchmark, Attribution88, is collected for robust and practical image attribution. We then propose RepMix, our GAN fingerprinting technique based on representation mixing and a novel loss. We validate its capability of tracing the provenance of GAN-generated images invariant to the semantic content of the image and also robust to perturbations. We show our approach improves significantly from existing GAN fingerprinting works on both semantic generalization and robustness. Data and code are available at https://github.com/TuBui/image_attribution .
Variational Flow Graphical Model

This paper introduces a novel approach embedding flow-based models in hierarchical structures. The proposed model learns the representation of high-dimensional data via a message-passing scheme by integrating flow-based functions through variational inference. Meanwhile, our model produces a representation of the data using a lower dimension, thus overcoming the drawbacks of many flow-based models, usually requiring a high dimensional latent space involving many trivial variables. With the proposed aggregation nodes, our model provides a new approach for distribution modeling and numerical inference on datasets. Multiple experiments on synthetic and real-world datasets show the benefits of our~proposed~method and potentially broad applications.
Modified generalized neo-fuzzy system with combined online fast learning in medical diagnostic task for situations of information deficit

In the paper, we propose the modified generalized neo-fuzzy system. It is designed to solve the pattern-image recognition task by working with data that are fed to the system in the image form. The neo-fuzzy system can work with small training datasets, where classes can overlap in a features space. The core of the system under consideration is a modification of multidimensional generalized neuro-fuzzy neuron with an additional softmax activation function in the output layer instead of the defuzzification layer and quartic-kernel functions as membership ones. The learning procedure of the system combined cross-entropy criterion optimization using a matrix version of the optimal by speed Kaczmarz-Widrow-Hoff algorithm with the additional filtering (smoothing) properties. In comparison to the well-known systems, the modified neo-fuzzy one provides both numerical and computational implementation simplicity. The computational experiments have proved the effectiveness of the modified generalized neo-fuzzy-neuron, including the situation with shot training datasets.
Big Data Analysis and Data Visualization to Help Make a Decision - Islamic Banks Case Study

Interest in data of various forms and sizes has risen in recent years, and it has become a defining aspect of modern period. The use of digital transformation strategies and their role in recovering from the consequences of the Corona pandemic, particularly in financial institutions, is the focus of this case study on Islamic banks. This study examines the big data analysis cycle for five Islamic banks’ data in the 2019/2020 era. In addition to following the five steps of the big data analysis cycle and concentrating on the phase of developing a model in order to produce graphical results that can be studied. It will make use of Google Data Studio, which is one of the greatest tools for analyzing large amounts of data. On the other hand, after creating the relevant hypotheses, it will explore possible scenarios and the visuals that result. Finally, there are visualizations and reports that assist decision-makers and investors in evaluating bank performance before to and during the Corona pandemic, making it easier to follow financial performance and conduct of banks. The research also considers the consequences of bank graphical reporting and considers whether hypotheses to help capture all statistical results in visual form are required.