Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x

Artificial intelligence (AI)-based computer-aided detection/diagnosis (CADe/x) has helped improve radiologists' performance and provides results equivalent or superior to those of radiologists' alone. This prospective multicenter cohort study aims to generate real-world evidence on the overall benefits and disadvantages of using AI-based CADe/x for breast cancer detection in a population-based breast cancer screening program comprising Korean women aged ≥ 40 years. The purpose of this report is to compare the diagnostic accuracy of radiologists with and without the use of AI-based CADe/x in mammography readings for breast cancer screening of Korean women with average breast cancer risk.Approximately 32,714 participants will be enrolled between February 2021 and December 2022 at 5 study sites in Korea. A radiologist specializing in breast imaging will interpret the mammography readings with or without the use of AI-based CADe/x. If recall is required, further diagnostic workup will be conducted to confirm the cancer detected on screening. The findings will be recorded for all participants regardless of their screening status to identify study participants with breast cancer diagnosis within both 1 year and 2 years of screening. The national cancer registry database will be reviewed in 2026 and 2027, and the results of this study are expected to be published in 2027. In addition, the diagnostic accuracy of general radiologists and radiologists specializing in breast imaging from another hospital with or without the use of AI-based CADe/x will be compared considering mammography readings for breast cancer screening.The Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM) study is a prospective multicenter study that aims to compare the diagnostic accuracy of radiologists with and without the use of AI-based CADe/x in mammography readings for breast cancer screening of women with average breast cancer risk. AI-STREAM is currently in the patient enrollment phase.ClinicalTrials.gov Identifier: NCT05024591.
Data privacy during pandemics: a systematic literature review of COVID-19 smartphone applications

On January 8, 2020, the Centers for Disease Control and Prevention officially announced a new virus in Wuhan, China. The first novel coronavirus (COVID-19) case was discovered on December 1, 2019, implying that the disease was spreading quietly and quickly in the community before reaching the rest of the world. To deal with the virus' wide spread, countries have deployed contact tracing mobile applications to control viral transmission. Such applications collect users' information and inform them if they were in contact with an individual diagnosed with COVID-19. However, these applications might have affected human rights by breaching users' privacy.This systematic literature review followed a comprehensive methodology to highlight current research discussing such privacy issues. First, it used a search strategy to obtain 808 relevant papers published in 2020 from well-established digital libraries. Second, inclusion/exclusion criteria and the snowballing technique were applied to produce more comprehensive results. Finally, by the application of a quality assessment procedure, 40 studies were chosen.This review highlights privacy issues, discusses centralized and decentralized models and the different technologies affecting users' privacy, and identifies solutions to improve data privacy from three perspectives: public, law, and health considerations.Governments need to address the privacy issues related to contact tracing apps. This can be done through enforcing special policies to guarantee users privacy. Additionally, it is important to be transparent and let users know what data is being collected and how it is being used.
Optimal text-to-image synthesis model for generating portrait images using generative adversarial network techniques

The advancements in artificial intelligence research, particularly in computer vision, have led to the development of previously unimaginable applications, such as generating new contents based on text description. In our work we focused on the text-to-image synthesis applications (TIS) field, to transform descriptive sentences into a real image. To tackle this issue, we use unsupervised deep learning networks that can generate high quality images from text descriptions, provided by eyewitnesses to assist law enforcement in their investigations, for the purpose of generating probable human faces. We analyzed a number of existing approaches and chose the best one. Deep fusion generative adversarial networks (DF-GAN) is the network that performs better than its peers, at multiple levels, like the generated image quality or the respect of the giving descriptive text. Our model is trained on the CelebA dataset and text descriptions (generated by our algorithm using existing attributes in the dataset). The obtained results from our implementation show that the learned generative model makes excellent quantitative and visual performances, the model is capable of generating realistic and diverse samples for human faces and create a complete portrait with respect of given text description.
Contrast Enhancement of Colour Images by Optimized Fuzzy Intensification

Contrast enhancement is a critical and difficult issue because inappropriate enhancement by existing global image enhancement techniques might result in over or under enhancement. Varying areas of the image that are lighted indicate different shades and contrast in the output images. Projected technique uses local colour correction in the Hue Saturation Luminance (HSL) colour space. To control colour fidelity in initial phase an optimized fuzzy intensification parameters are extracted automatically form fuzzy inference system for that particular image. Finally optimized Fuzzy Intensification parameter constants are used to minimize overexposed and underexposed areas and offers elevated contrast improvement. Several lab test conducted to analyze the effectiveness of the proposed method with existing strategies. Many quality evaluation parameters are evaluated, and findings are compared to some known colour picture contrast enhancement approaches. The produced output comparatively better than many existing techniques which support a moderate measure to visual perception of the processed images.
Higher Order Thinking Skills Based Learning Outcomes Improvement with Blended Web Mobile Learning Model

The objective of this research is to analyze the effectiveness of the blended web mobile learning model in improving Higher Order Thinking Skills-based learning outcomes of high school students.This research is a quasi-experiment design.The research sample was 137 students from 2 senior high schools in East Java Province -Indonesia.Before implementing the BWML model using MoLearn, both groups of students were tested (pre-test) and after the learning was complete, students were again given the same test (post-test).The collected data were analyzed using the Wilcoxon test; calculation of n-gain; and the Mann-Whitney U test.The results shows that biology learning using the BWML Model is effective in improving student learning outcomes as indicated by: (1) an increase in student learning outcomes which is statistically significant at =5%, (2) the average of n-gain is in the medium category, and (3) the mean of n-gain has no difference in the two groups of students.
Neuron Coverage-Guided Domain Generalization

This paper focuses on the domain generalization task where domain knowledge is unavailable, and even worse, only samples from a single domain can be utilized during training. Our motivation originates from the recent progresses in deep neural network (DNN) testing, which has shown that maximizing neuron coverage of DNN can help to explore possible defects of DNN (i.e., misclassification). More specifically, by treating the DNN as a program and each neuron as a functional point of the code, during the network training we aim to improve the generalization capability by maximizing the neuron coverage of DNN with the gradient similarity regularization between the original and augmented samples. As such, the decision behavior of the DNN is optimized, avoiding the arbitrary neurons that are deleterious for the unseen samples, and leading to the trained DNN that can be better generalized to out-of-distribution samples. Extensive studies on various domain generalization tasks based on both single and multiple domain(s) setting demonstrate the effectiveness of our proposed approach compared with state-of-the-art baseline methods. We also analyze our method by conducting visualization based on network dissection. The results further provide useful evidence on the rationality and effectiveness of our approach.
ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables

In many anomaly detection tasks, where anomalous data rarely appear and are difficult to collect, training using only normal data is important. Although it is possible to manually create anomalous data using prior knowledge, they may be subject to user bias. In this paper, we propose an Anomalous Latent variable Generative Adversarial Network (ALGAN) in which the GAN generator produces pseudo-anomalous data as well as fake-normal data, whereas the discriminator is trained to distinguish between normal and pseudo-anomalous data. This differs from the standard GAN discriminator, which specializes in classifying two similar classes. The training dataset contains only normal data; the latent variables are introduced in anomalous states and are input into the generator to produce diverse pseudo-anomalous data. We compared the performance of ALGAN with other existing methods on the MVTec-AD, Magnetic Tile Defects, and COIL-100 datasets. The experimental results showed that ALGAN exhibited an AUROC comparable to those of state-of-the-art methods while achieving a much faster prediction time.
Prediction-Guided Distillation for Dense Object Detection

Real-world object detection models should be cheap and accurate. Knowledge distillation (KD) can boost the accuracy of a small, cheap detection model by leveraging useful information from a larger teacher model. However, a key challenge is identifying the most informative features produced by the teacher for distillation. In this work, we show that only a very small fraction of features withinCrowley, Elliot J. a ground-truth bounding box are responsible for a teacher’s high detection performance. Based on this, we propose Prediction-Guided Distillation (PGD), which focuses distillation on these key predictive regions of the teacher and yields considerable gains in performance over many existing KD baselines. In addition, we propose an adaptive weighting scheme over the key regions to smooth out their influence and achieve even better performance. Our proposed approach outperforms current state-of-the-art KD baselines on a variety of advanced one-stage detection architectures. Specifically, on the COCO dataset, our method achieves between +3.1% and +4.6% AP improvement using ResNet-101 and ResNet-50 as the teacher and student backbones, respectively. On the CrowdHuman dataset, we achieve +3.2% and +2.0% improvements in MR and AP, also using these backbones. Our code is available at https://github.com/ChenhongyiYang/PGD .
RETE: Retrieval-Enhanced Temporal Event Forecasting on Unified Query Product Evolutionary Graph

With the increasing demands on e-commerce platforms, numerous user action history is emerging. Those enriched action records are vital to understand users’ interests and intents. Recently, prior works for user behavior prediction mainly focus on the interactions with product-side information. However, the interactions with search queries, which usually act as a bridge between users and products, are still under investigated. In this paper, we explore a new problem named temporal event forecasting, a generalized user behavior prediction task in a unified query product evolutionary graph, to embrace both query and product recommendation in a temporal manner. To fulfill this setting, there involves two challenges: (1) the action data for most users is scarce; (2) user preferences are dynamically evolving and shifting over time. To tackle those issues, we propose a novel Retrieval-Enhanced Temporal Event (RETE) forecasting framework. Unlike existing methods that enhance user representations via roughly absorbing information from connected entities in the whole graph, RETE efficiently and dynamically retrieves relevant entities centrally on each user as high-quality subgraphs, preventing the noise propagation from the densely evolutionary graph structures that incorporate abundant search queries. And meanwhile, RETE autoregressively accumulates retrieval-enhanced user representations from each time step, to capture evolutionary patterns for joint query and product prediction. Empirically, extensive experiments on both the public benchmark and four real-world industrial datasets demonstrate the effectiveness of the proposed RETE method.
MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective

NER model has achieved promising performance on standard NER benchmarks.However, recent studies show that previous approaches may over-rely on entity mention information, resulting in poor performance on out-of-vocabulary (OOV) entity recognition.In this work, we propose MINER, a novel NER learning framework, to remedy this issue from an information-theoretic perspective.The proposed approach contains two mutual information-based training objectives: i) generalizing information maximization, which enhances representation via deep understanding of context and entity surface forms; ii) superfluous information minimization, which discourages representation from rote memorizing entity names or exploiting biased cues in data.Experiments on various settings and datasets demonstrate that it achieves better performance in predicting OOV entities.
Local Path Planning of the Autonomous Vehicle Based on Adaptive Improved RRT Algorithm in Certain Lane Environments

Given that the rapidly exploring random tree algorithm (RRT) and its variants cannot efficiently solve problems of path planning of autonomous vehicles, this paper proposes a new, adaptive improved RRT algorithm. Firstly, an adaptive directional sampling strategy is introduced to avoid excessive search by reducing the randomness of sampling points. Secondly, a reasonable node selection strategy is used to improve the smoothness of the path by utilizing a comprehensive criterion that combines angle and distance. Thirdly, an adaptive node expansion strategy is utilized to avoid invalid expansion and make the generated path more reasonable. Finally, the expanded ellipse is used to realize vehicle obstacle avoidance in advance, and the post-processing strategy removes redundant line segments of the initial path to improve its quality. The simulation results show that the quality of the planned path is significantly improved. This path followed successfully has good trajectory stability, which shows the proposed algorithm’s effectiveness and practicability in autonomous vehicles’ local path planning.
The spectrum of blockchain adoption for developing business model innovation

Purpose A blockchain (BC) is a breakthrough technological invention that comprises entirely different mental models than conventional technology. This fundamental difference can potentially change the systems of many organizations since the current systems are built upon a centralized paradigm. The adoption of BC brings various benefits to an organization which can initiate changes to a business model (BM). However, the contribution of BC for business model innovation (BMI) is challenging to identify. Therefore, this study aims to understand and describe the adoption of BC for developing BMI. Design/methodology/approach This study presents a model that describes the adoption of BC for developing BMI. To justify the model, this study used an empirical approach based on multiple case study through a rigorous process. The case study selection process referred to the products or services that adopt BC to deliver to their customers and monetize their businesses, which resulted in six cases in different areas. Meanwhile, the data collection applied semi-structured interviews and adequate secondary data. The data/information was analyzed using a value proposition, creation, and capture framework. Findings The findings identify the adoption of BC in BMIs generated through value creation as a new technological sub-element. This technological adoption evidently affects value proposition and value capture in a different mode. Furthermore, through the model, this study classifies the adoption of BC in BMI based on two dimensions: (1) the level of complexity of BC adoption and (2) the intensity of BMI. The findings show that the cases in this study are dispersed among all quadrants of the conceptual model. Originality/value This study can serve as an antecedent for stakeholders in the innovation of BC-based BMs and their implementation patterns. Simultaneously, this study sheds light on the body of knowledge about BC adoption for developing BMI through a validated model from selected cases and technical experts. This study also describes the BC-based activity systems that provide the contributions and benefits from the technology.
Distributionally-robust Recommendations for Improving Worst-case User Experience

Modern recommender systems have evolved rapidly along with deep learning models that are well-optimized for overall performance, especially those trained under Empirical Risk Minimization (ERM). However, a recommendation algorithm that focuses solely on the average performance may reinforce the exposure bias and exacerbate the “rich-get-richer” effect, leading to unfair user experience. In a simulation study, we demonstrate that such performance gap among various user groups is enlarged by an ERM-trained recommender in the long-term. To mitigate such amplification effects, we propose to optimize for the worst-case performance under the Distributionally Robust Optimization (DRO) framework, with the goal of improving long-term fairness for disadvantaged subgroups. In addition, we propose a simple-yet-effective streaming optimization improvement called Streaming-DRO (S-DRO), which effectively reduces loss variances for recommendation problems with sparse and long-tailed data distributions. Our results on two large-scale datasets suggest that (1) DRO is a flexible and effective technique for improving worst-case performance, and (2) Streaming-DRO outperforms vanilla DRO and other strong baselines by improving the worst-case and overall performance at the same time.
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment

Predicting missing facts in a knowledge graph (KG) is crucial as modern KGs are far from complete.Due to labor-intensive human labeling, this phenomenon deteriorates when handling knowledge represented in various languages.In this paper, we explore multilingual KG completion, which leverages limited seed alignment as a bridge, to embrace the collective knowledge from multiple languages.However, language alignment used in prior works is still not fully exploited: (1) alignment pairs are treated equally to maximally push parallel entities to be close, which ignores KG capacity inconsistency; (2) seed alignment is scarce and new alignment identification is usually in a noisily unsupervised manner.To tackle these issues, we propose a novel self-supervised adaptive graph alignment (SS-AGA) method.Specifically, SS-AGA fuses all KGs as a whole graph by regarding alignment as a new edge type.As such, information propagation and noise influence across KGs can be adaptively controlled via relation-aware attention weights.Meanwhile, SS-AGA features a new pair generator that dynamically captures potential alignment pairs in a self-supervised paradigm.Extensive experiments on both the public multilingual DBPedia KG and newly-created industrial multilingual E-commerce KG empirically demonstrate the effectiveness of SS-AGA 1 .
Beyond dyadic HRI

research-article Open Access Share on Beyond dyadic HRI: building robots for society Authors: Eva Hornecker Bauhaus-Universität Weimar Bauhaus-Universität WeimarView Profile , Antonia Krummheuer Aalborg University Aalborg UniversityView Profile , Andreas Bischof Technische Universität Chemnitz Technische Universität ChemnitzView Profile , Matthias Rehm Aalborg University Aalborg UniversityView Profile Authors Info & Claims InteractionsVolume 29Issue 3May - June 2022 pp 48–53https://doi.org/10.1145/3526119Online:02 May 2022Publication History 0citation3,736DownloadsMetricsTotal Citations0Total Downloads3,736Last 12 Months3,736Last 6 weeks103 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 SiteView all FormatsPDF
OPERA: Operation-Pivoted Discrete Reasoning over Text

Machine reading comprehension (MRC) that requires discrete reasoning involving symbolic operations, e.g., addition, sorting, and counting, is a challenging task.According to this nature, semantic parsing-based methods predict interpretable but complex logical forms.However, logical form generation is nontrivial and even a little perturbation in a logical form will lead to wrong answers.To alleviate this issue, multi-predictor -based methods are proposed to directly predict different types of answers and achieve improvements.However, they ignore the utilization of symbolic operations and encounter a lack of reasoning ability and interpretability.To inherit the advantages of these two types of methods, we propose OPERA, an operation-pivoted discrete reasoning framework, where lightweight symbolic operations (compared with logical forms) as neural modules are utilized to facilitate the reasoning ability and interpretability.Specifically, operations are first selected and then softly executed to simulate the answer reasoning procedure.Extensive experiments on both DROP 1 and RACENum datasets show the reasoning ability of OPERA.Moreover, further analysis verifies its interpretability.