Reviewerly/exHarmony huggingface dataset:
Feature Extraction Using GLSM for DDSM Mammogram Images

One of the worst illnesses for women is breast cancer. Around 2.1 million women have this cancer per year. The prevalence of breast cancer ranges across nations, but in most instances, the second cause of death for the female population is this form of cancer. Mammography research, which shapes the experts’ auto-exploration and manual exploration, is the most powerful technology for the early diagnosis of breast cancer. In the detection of calcifications and masses in the mammography photograph, there is a considerable research effort, apart from other potential anomalies, since these two forms of artefacts are the best markers of a possible early stage of breast cancer. Microcalcifications present in the optical mammograms as subtle and light spots from 0.3 mm and 1 mm in scale. The naked eye is not possible to observe since the thick breast tissue covering it renders suspicious regions nearly opaque. If cancer is diagnosed early by using breast self-examination (BSE) and clinical breast examination (CBE) at 40–49 years of age, the survival rate of breast cancer reaches 100 per cent. New strategies named CAD (computer-assisted diagnosis) programmes for early detection utilising multiple mammogram datasets, such as mini-Mias, DDSM, etc., have been discovered by several researchers. As mammography appears to be deemed a unique screening technique for breast lesion identification and evaluation, it uses lower radiation doses than ordinary chest X-rays. In this paper, DDSM dataset is used. The aim of this system is to extract features to differentiate between different categories of images like normal, benign and malignant. Dataset has tags and extra component which is not useful for processing, so all unnecessary components are removed by global thresholding method. Wavelet transform is the most popular transform with the combination of wavelet and GLCM eight significant features are extracted from mammogram images. It is observed that from features, it is possible to differentiate between normal, benign and malignant images. These features can further apply to best machine learning algorithm to check the accuracy of the system.
VisiFit: Structuring Iterative Improvement for Novice Designers

Visual blends are an advanced graphic design technique to seamlessly integrate two objects into one. Existing tools help novices create prototypes of blends, but it is unclear how they would improve them to be higher fidelity. To help novices, we aim to add structure to the iterative improvement process. We introduce a method for improving prototypes that uses secondary design dimensions to explore a structured design space. This method is grounded in the cognitive principles of human visual object recognition. We present VisiFit – a computational design system that uses this method to enable novice graphic designers to improve blends with computationally generated options they can select, adjust, and chain together. Our evaluation shows novices can substantially improve 76% of blends in under 4 minutes. We discuss how the method can be generalized to other blending problems, and how computational tools can support novices by enabling them to explore a structured design space quickly and efficiently.
Multi-Modal Label Dequantized Gaussian Process Latent Variable Model for Ordinal Label Estimation

This paper presents multi-modal label dequantized Gaussian process latent variable model (mLDGP) for ordinal label estimation. mLDGP is constructed based on a probabilistic generative model via Gaussian process and realizes accurate calculation of common latent space from multi-view features including low-dimensional ordinal label features. Conventional methods have a problem that the dimension of the common latent space was limited to that of the label feature, and an enough expressive latent space cannot be obtained. mLDGP, which is constructed by introducing our novel label de-quantization mechanism into the objective function of multi-modal Gaussian process latent variable model (GPLVM), can increase the dimension of label features. Then mLDGP can calculate the effective latent space. Furthermore, mLDGP can estimate projection transforming unknown features of test samples into the common latent space, which was a problem of the conventional GPLVMs. From experimental results obtained by applying our method to the product rating estimation on the online shopping website, it is confirmed that accuracy improvement using mLDGP becomes feasible compared to various methods.
Patient Empowerment During the COVID-19 Pandemic by Ensuring Safe and Fast Communication of Test Results: Implementation and Performance of a Tracking System

Background Overcoming the COVID-19 crisis requires new ideas and strategies for online communication of personal medical information and patient empowerment. Rapid testing of a large number of subjects is essential for monitoring and delaying the spread of SARS-CoV-2 in order to mitigate the pandemic’s consequences. People who do not know that they are infected may not stay in quarantine and, thus, risk infecting others. Unfortunately, the massive number of COVID-19 tests performed is challenging for both laboratories and the units that conduct throat swabs and communicate the results. Objective The goal of this study was to reduce the communication burden for health care professionals. We developed a secure and easy-to-use tracking system to report COVID-19 test results online that is simple to understand for the tested subjects as soon as these results become available. Instead of personal calls, the system updates the status and the results of the tests automatically. This aims to reduce the delay when informing testees about their results and, consequently, to slow down the virus spread. Methods The application in this study draws on an existing tracking tool. With this open-source and browser-based online tracking system, we aim to minimize the time required to inform the tested person and the testing units (eg, hospitals or the public health care system). The system can be integrated into the clinical workflow with very modest effort and avoids excessive load to telephone hotlines. Results The test statuses and results are published on a secured webpage, enabling regular status checks by patients; status checks are performed without the use of smartphones, which has some importance, as smartphone usage diminishes with age. Stress tests and statistics show the performance of our software. CTest is currently running at two university hospitals in Germany—University Hospital Ulm and University Hospital Tübingen—with thousands of tests being performed each week. Results show a mean number of 10 (SD 2.8) views per testee. Conclusions CTest runs independently of existing infrastructures, aims at straightforward integration, and aims for the safe transmission of information. The system is easy to use for testees. QR (Quick Response) code links allow for quick access to the test results. The mean number of views per entry indicates a reduced amount of time for both health care professionals and testees. The system is quite generic and can be extended and adapted to other communication tasks.
A Fast and Efficient Network for Single Image Deraining

Rain streaks will degrade the visibility of images. To tackle this problem, we propose a novel Adaptive Dilated Network (ADN) to remove rain streaks from a single image while using less parameters and running faster than previous methods. Specifically, an Adaptive Dilated Block (ADB) is constructed as the sub-module of ADN. In ADB, we apply a shared dilated block to extract multi-scale features. Then a dilated selection block is added to leverage the importance of features in different scales. All the multi-scale features are fused together to obtain features with rich rain details. To further model the inter-dependencies of the fused features, a feature selection block is employed in ADB to assign different weights to each feature. Moreover, all the hierarchical features extracted by each ADB are concatenated together and fed into a rainy map generator to estimate rain layer. Experimental results demonstrate that the proposed method is superior to the state-of-the-art methods on performances and running time while using less parameters. The source code is available at https://github.com/nnUyi/ADN.
Robustness analysis of Bitcoin confirmation times

Bitcoin payments require a random amount of time to get confirmed (i.e. to be grouped by the miners into a block and to be added to the Bitcoin blockchain). In [8, 11], the authors propose the modelling of the Bitcoin confirmation time by the so-called time to ruin of the Cramer-Lundberg (CL) model. This provides off-the-shelf results directly aimed at predicting the confirmation time. However, analyses suggest that the data may not fully conform with the CL model assumptions. In this manuscript, we show by means of a robustness analysis that the time to ruin of a CL model is near insensitive to small changes in the model assumptions and illustrate that the proposed heuristic model can be used to accurately predict the confirmation times even when the data deviate (to a small degree) from the model assumptions.
Affective awareness in neural sentiment analysis

Sentiment analysis is helpful to bestow ability of understanding human’s attitude in texts on artificial intelligence systems. In this area, text sentiment is usually signaled by a few indicative words that convey affective meanings and arouse readers’ collective emotions. However, most existing sentiment analysis models have predominantly featured through neural network architectures with end-to-end training manner and limited awareness of affective knowledge, which, as a result, often fails to pinpoint the essential features for sentiment prediction. In this work, we present a novel approach for sentiment analysis by fusing external affective knowledge into neural networks. The affective knowledge is distilled from two sentiment lexicons grounded by two psychological theories, e.g., the Affect Control Theory and word affections in terms of Valence, Arousal, and Dominance. To examine the effects of affective knowledge over sentiment analysis, we conduct cross-dataset and cross-model experiments along with a detailed ablation analysis. Results show that our proposed method outperforms trendy neural networks in all the five benchmarks with consistent and significant improvement (1.4% Accuracy in average). Further discussions demonstrate that all affective attributes exhibit positive effects to model enhancement and our model is robust to the change of lexicon size.
On the Marginal Benefit of Active Learning: Does Self-Supervision Eat its Cake?

Active learning is the set of techniques for intelligently labeling large unlabeled datasets to reduce the labeling effort. In parallel, recent developments in self-supervised and semi-supervised learning (S4L) provide powerful techniques, based on data-augmentation, contrastive learning, and self-training, that enable superior utilization of unlabeled data which led to a significant reduction in required labeling in the standard machine learning benchmarks. A natural question is whether these paradigms can be unified to obtain superior results. To this aim, this paper provides a novel algorithmic framework integrating self-supervised pretraining, active learning, and consistency-regularized self-training. We conduct extensive experiments with our framework on CIFAR10 and CIFAR100 datasets. These experiments enable us to isolate and assess the benefits of individual components which are evaluated using state-of-the-art methods (e.g. Core-Set, VAAL, simCLR, FixMatch). Our experiments reveal two key insights: (i) Self-supervised pre-training significantly improves semi-supervised learning, especially in the few-label regime, (ii) The benefit of active learning is undermined and subsumed by S4L techniques. Specifically, we fail to observe any additional benefit of state-of-the-art active learning algorithms when combined with state-of-the-art S4L techniques.
Enhancing Decoding Rate of Barcode Decoders in Complex Scenes for IoT Systems

Camera-based multiclass (1-D and 2-D) barcode detectors that can help in decoding barcodes in different complex scenes have huge potential applications in situations where Internet of Things (IoT) is combined with artificial intelligence (AI) and augmented reality (AR). The decoding rate in such applications under real-life complex scenes is greatly affected by two major factors: first, we cannot accurately localize the barcodes, and second, we cannot decode the blur samples. In this article, we first propose a barcode localization algorithm that is capable of regressing four vertices of barcodes accurately. Our localization method comprises an anchor-free approach that outputs multiscale output prediction maps. These segmentation-like maps of each scale are then further divided into three types of maps (classification, centerness, and 8-D regression). Eight-dimensional localization result of barcodes along with classification result is then obtained after postprocessing. Second, we propose a conditional generative adversarial network-based model for deblurring blur QR codes. Extensive decoding experiments on a challenging complex scene data set show that our localization and deblurring methods can contribute to improving the decoding rate of existing barcode decoders.
The influence of cybersickness and the media devices’ mobility on the persuasive effects of 360° commercials

Abstract With the rise of immersive media, advertisers have started to use 360° commercials to engage and persuade consumers. Two experiments were conducted to address research gaps and to validate the positive impact of 360° commercials in realistic settings. The first study ( N = 62) compared the effects of 360° commercials using either a mobile cardboard head-mounted display (HMD) or a laptop. This experiment was conducted in the participants’ living rooms and incorporated individual feelings of cybersickness as a moderator. The participants who experienced the 360° commercial with the HMD reported higher spatial presence and product evaluation, but their purchase intentions were only increased when their reported cybersickness was low. The second experiment ( N = 197) was conducted online and analyzed the impact of 360° commercials that were experienced with mobile (smartphone/tablet) or static (laptop/desktop) devices instead of HMDs. The positive effects of omnidirectional videos were stronger when participants used mobile devices.
Review of Blockchain Security and Privacy

Blockchain is a progressive innovation technology that permits individuals to record message exchanges or transactions on an advanced, decentralized, distributed ledger, with no central controlling authority as in case of financial banking systems. Recorded transactions are seen to all users within the blockchain network and cannot be modified by any user or node. Blockchain has become popular with many other applications including IoT, healthcare, industry, supply chain management etc. Blockchain technology is ready to change almost every aspect of our advanced digital lives. By obviating third parties, blockchains guarantee to make our frameworks more effective. By going around oversight, they guarantee to make our frameworks more impartial. Also, if appropriately executed, they could make our frameworks more dependable and secure. The purpose of this review is to understand blockchain technology and challenges associated with its security and privacy.
Using digital image correlation for in situ strain and damage monitoring in hybrid fiber laminates under in‐plane shear loading

Abstract Utilizing resistive strain gauges for V‐notch shear tests in fiber reinforced laminates is usually limited to low strain values due to technical uncertainties imposed by the measurement technique and also the progressive damage development in the notched region of the composite. Therefore, this study investigates an alternative approach by using digital image correlation system and further comparison with resistive strain gauge to identify the effect of heterogenous nature of damage development on measurement results. Moreover, glass/carbon fiber hybrid and non‐hybrid laminates are tested in two major directions through the same approach to determine the effect of stacking sequence in spatial microdamage accumulation. It is shown that tailoring appropriate region of interest for full field strain measurement at various stages of loading enables proper monitoring of microdamage accumulation and thereby delivers a precise stress–strain behavior for hybrid and non‐hybrid laminates. Comparison of the strain maps for hybrid laminates tested in 0° direction, shows that the presence of carbon plies at the surface results in uniform strain distribution between the notches of the test sample. In contrast, a non‐homogeneous strain map with separate regions of high strain gradient are observed for hybrid specimens with glass plies at surface, which can cause underestimation of average shear strain by resistive strain gauges. On the other hand, it is shown that strain maps for hybrid samples tested at 90° configuration is analogous regardless of the stacking sequence, and damage accumulation in these samples is most likely related to relative volume fraction of various fibers in hybrid laminate.
Verification of Image-Based Neural Network Controllers Using Generative Models

Although neural networks are effective tools for processing information from image-based sensors to produce control actions, their complex nature limits their use in safety-critical systems. For this reason, recent work has focused on combining techniques in formal methods and reachability analysis to obtain guarantees on the closed-loop performance of neural network controllers. However, these techniques do not scale to the high-dimensional and complicated input space of image-based neural network controllers. This work proposes a method to address these challenges by training a generative adversarial network to map states to plausible input images. Concatenating the generator network with the control network results in a network with a low-dimensional input space, which allows for the use of existing closed-loop verification tools to obtain formal guarantees on the performance of image-based controllers. This approach is applied to provide safety guarantees for an image-based neural network controller for an autonomous aircraft taxi problem. The resulting guarantees are with respect to the set of input images modeled by the generator network, and so a recall metric is provided to evaluate how well the generator captures the space of plausible images.
Action State Update Approach to Dialogue Management

Utterance interpretation is one of the main functions of a dialogue manager, which is the key component of a dialogue system. We propose the action state update approach (ASU) for utterance interpretation, featuring a statistically trained binary classifier used to detect dialogue state update actions in the text of a user utterance. Our goal is to interpret referring expressions in user input without a domain-specific natural language understanding component. For training the model, we use active learning to automatically select simulated training examples. With both user-simulated and interactive human evaluations, we show that the ASU approach successfully interprets user utterances in a dialogue system, including those with referring expressions.
Global-Local Attention Network for Semantic Segmentation in Aerial Images

Errors in semantic segmentation could be classified into two types: the large area misclassification and inaccurate local boundaries. Previously attention-based methods typically capture rich global contextual information, which benefits the large area classification but cannot address the local errors of boundaries. In this paper, we propose a Global-Local Attention Network (GLANet) which can simultaneously consider the global context and local details. Specifically, our GLANet consists of two branches: (1) the global attention branch and (2) local attention branch. Furthermore, three different modules are embedded in GLANet for respectively modelling the semantic interdependencies in spatial, channel and boundary dimension. Lastly, we merge the outputs of different branches to enhance the feature representation further, resulting in more precise segmentation. Overall, the proposed method achieves the competitive segmentation accuracy on two public aerial image datasets, bringing significant improvements over the existing baselines.
An Improved Deep Relation Network for Action Recognition in Still Images

Contextual information has been widely utilized in visual recognition tasks. This is especially true for action recognition, because contextual information such as objects interacting with human and the scene where the action is performed is inseparable from action categories. To this end, we propose an efficient relation module that combines Human-Object and Scene-Object relations for action recognition. Specifically, Human-Object interaction submodule can capture more accurate appearance and spatial relation to build human-object interaction pairs. And Scene- Object interaction submodule can learn the probability of the objects involved in the scene to help discover the key interaction pair. We conduct extensive experiments on Stanford 40 and Pascal Voc 2012 Action datasets to verify our model, and experimental results show that our method achieves superior performance on these two datasets. Especially, we gain the best results on the Stanford 40 dataset compared with state-of-the-arts.
Do You Get What I Mean?!? The Undesirable Outcomes of (Ab)Using Paralinguistic Cues in Computer-Mediated Communication

The shift to working from home, which has intensified due to Covid-19, increased our reliance on communication technology and the need to communicate effectively via computer-mediated communication and especially via text. Paralinguistic cues, such as repeated punctuation, are used to compensate for the lack of non-verbal cues in text-based formats. However, it is unclear whether these cues indeed bridge the potential gap between the writer's intentions and the reader's interpretations. A pilot study and two experiments investigated the effect of using repeated punctuation on behavioral intention to assist an email writer in a work-related situation. Findings demonstrate that while the intentions behind using repeated punctuation relate to signaling situational importance or affective state, behavioral intentions are driven by dispositional rather than situational attributions. Specifically, the use of repeated punctuation reduces perceived competence of the message writer and consequently decreases positive behavioral intentions. Overall, the study challenges the simplified view of paralinguistic cues as communication facilitators, highlighting their potential harmful effects on impression formation and behavioral intentions in the digital age.