Reviewerly/exHarmony huggingface dataset:
Prosociality and the Uptake of COVID-19 Contact Tracing Apps: Survey Analysis of Intergenerational Differences in Japan

Background To control the COVID-19 pandemic, it is essential to trace and contain infection chains; for this reason, policymakers have endorsed the usage of contact tracing apps. To date, over 50 countries have released such apps officially or semiofficially, but those that rely on citizens’ voluntary uptake suffer from low adoption rates, reducing their effectiveness. Early studies suggest that the low uptake is driven by citizens’ concerns about security and privacy, as well as low perceptions of infection risk and benefits from the usage. However, these do not explore important generational differences in uptake decision or the association between individuals’ prosociality and uptake. Objective The objective of our study was to examine the role of individuals’ prosociality and other factors discussed in the literature, such as perceived risk and trust in government, in encouraging the usage of contact tracing apps in Japan. We paid particular attention to generational differences. Methods A web-based survey was conducted in Japan 6 months after the release of a government-sponsored contact tracing app. Participants were recruited from individuals aged between 20 and 69 years. Exploratory factor analyses were conducted to measure prosociality, risk perception, and trust in government. Logistic regression was used to examine the association between these factors and uptake. Results There was a total of 7084 respondents, and observations from 5402 respondents were used for analysis, of which 791 respondents (14.6%) had ever used the app. Two factors of prosociality were retained: agreeableness and attachment to the community. Full-sample analysis demonstrated app uptake was determined by agreeableness, attachment to the community, concern about health risks, concern about social risks, and trust in the national government; however, important differences existed. The uptake decision of respondents aged between 20 and 39 years was attributed to their attachment to the community (odds ratio [OR] 1.28, 95% CI 1.11-1.48). Agreeable personality (OR 1.18, 95% CI 1.02-1.35), concern about social risk (OR 1.17, 95% CI 1.02-1.35), and trust in national government (OR 1.16, 95% CI 1.05-1.28) were key determinants for those aged between 40 and 59 years. For those aged over 60 years, concerns about health risks determined the uptake decision (OR 1.49, 95% CI 1.24-1.80). Conclusions Policymakers should implement different interventions for each generation to increase the adoption rate of contact tracing apps. It may be effective to inform older adults about the health benefits of the apps. For middle-age adults, it is important to mitigate concerns about security and privacy issues, and for younger generations, it is necessary to boost their attachment to their community by utilizing social media and other web-based network tools.
L3DOC: Lifelong 3D Object Classification

3D object classification has been widely applied in both academic and industrial scenarios. However, most state-of-the-art algorithms rely on a fixed object classification task set, which cannot tackle the scenario when a new 3D object classification task is coming. Meanwhile, the existing lifelong learning models can easily destroy the learned tasks performance, due to the unordered, large-scale, and irregular 3D geometry data. To address these challenges, we propose a Lifelong 3D Object Classification (i.e., L3DOC) model, which can consecutively learn new 3D object classification tasks via imitating “human learning”. More specifically, the core idea of our model is to capture and store the cross-task common knowledge of 3D geometry data in a 3D neural network, named as point-knowledge, through employing layer-wise point-knowledge factorization architecture. Afterwards, a task-relevant knowledge distillation mechanism is employed to connect the current task to previous relevant tasks and effectively prevent catastrophic forgetting. It consists of a point-knowledge distillation module and a transforming-space distillation module, which transfers the accumulated point-knowledge from previous tasks and soft-transfers the compact factorized representations of the transforming-space, respectively. To our best knowledge, the proposed L3DOC algorithm is the first attempt to perform deep learning on 3D object classification tasks in a lifelong learning way. Extensive experiments on several point cloud benchmarks illustrate the superiority of our L3DOC model over the state-of-the-art lifelong learning methods.
PIT: Position-Invariant Transform for Cross-FoV Domain Adaptation

Cross-domain object detection and semantic segmentation have witnessed impressive progress recently. Existing approaches mainly consider the domain shift resulting from external environments including the changes of background, illumination or weather, while distinct camera intrinsic parameters appear commonly in different domains and their influence for domain adaptation has been very rarely explored. In this paper, we observe that the Field of View (FoV) gap induces noticeable instance appearance differences between the source and target domains. We further discover that the FoV gap between two domains impairs domain adaptation performance under both the FoV-increasing (source FoV < target FoV) and FoV-decreasing cases. Motivated by the observations, we propose the Position-Invariant Transform (PIT) to better align images in different domains. We also introduce a reverse PIT for mapping the transformed/aligned images back to the original image space, and design a loss reweighting strategy to accelerate the training process. Our method can be easily plugged into existing cross-domain detection/segmentation frameworks, while bringing about negligible computational overhead. Extensive experiments demonstrate that our method can soundly boost the performance on both cross-domain object detection and segmentation for state-of-the-art techniques. Our code is available at https://github.com/sheepooo/PIT-Position-Invariant-Transform.
Deep multiple-instance learning for abnormal cell detection in cervical histopathology images

Cervical cancer is a disease of significant concern affecting women's health worldwide. Early detection of and treatment at the precancerous stage can help reduce mortality. High-grade cervical abnormalities and precancer are confirmed using microscopic analysis of cervical histopathology. However, manual analysis of cervical biopsy slides is time-consuming, needs expert pathologists, and suffers from reader variability errors. Prior work in the literature has suggested using automated image analysis algorithms for analyzing cervical histopathology images captured with the whole slide digital scanners (e.g., Aperio, Hamamatsu, etc.). However, whole-slide digital tissue scanners with good optical magnification and acceptable imaging quality are cost-prohibitive and difficult to acquire in low and middle-resource regions. Hence, the development of low-cost imaging systems and automated image analysis algorithms are of critical importance. Motivated by this, we conduct an experimental study to assess the feasibility of developing a low-cost diagnostic system with the H&E stained cervical tissue image analysis algorithm. In our imaging system, the image acquisition is performed by a smartphone affixing it on the top of a commonly available light microscope which magnifies the cervical tissues. The images are not captured in a constant optical magnification, and, unlike whole-slide scanners, our imaging system is unable to record the magnification. The images are mega-pixel images and are labeled based on the presence of abnormal cells. In our dataset, there are total 1331 (train: 846, validation: 116 test: 369) images. We formulate the classification task as a deep multiple instance learning problem and quantitatively evaluate the classification performance of four different types of multiple instance learning algorithms trained with five different architectures designed with varying instance sizes. Finally, we designed a sparse attention-based multiple instance learning framework that can produce a maximum of 84.55% classification accuracy on the test set.
Detecting fake hospitality reviews through the interplay of emotional cues, cognitive cues and review valence

Purpose This paper aims to examine the dynamics of emotional cues and cognitive cues in review fakeness. Additionally, the boundary condition (i.e. review valence) for the dynamics between emotional cues and cognitive cues is investigated. Design/methodology/approach This research conducted two studies, which analyzed restaurant and hotel reviews collected from Yelp.com. The authors adopted linguistic inquiry and word count 2015 to code review contents and tested the hypotheses using logistic regression. Findings Fake reviews contain more emotional cues compared with authentic reviews. Moreover, the dynamics of emotional cues and cognitive cues are salient among negative reviews. Practical implications This research provides implications to identify fake online reviews based on linguistic cues. Originality/value This research contributes to the literature by revealing the competition of mental resources between emotional and cognitive systems when deception is for harming others. Grounded in interpersonal deception theory, this paper investigates the interactive effect and complements the literature, which mainly used emotional cues and cognitive cues individually to detect fake reviews.
A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption

In the edge computing and network communication environment, important image data need to be transmitted and stored securely. Under the condition of limited computing resources, it is particularly necessary to design effective and fast image encryption algorithms. One-dimensional (1D) chaotic maps provide an effective solution for real-time image encryption, but most 1D chaotic maps have only one parameter and a narrow chaotic interval, which has the disadvantage of security. In this paper, a new compound 1D chaotic map composed of a logistic map and tent map is proposed. The new system has two system parameters and an arbitrarily large chaotic parameter interval, and its chaotic signal is evenly distributed in the whole value space so it can improve the security in the application of information encryption. Furthermore, based on the new chaotic system, a fast image encryption algorithm is proposed. The algorithm takes the image row (column) as the cyclic encryption unit, and the time overhead is greatly reduced compared with the algorithm taking the pixel as the encryption unit. In addition, the mechanism of intermediate key associated with image content is introduced to improve the ability of the algorithm to resist chosen-plaintext attack and differential attack. Experiments show that the proposed image encryption algorithm has obvious speed advantages and good cryptographic performance, showing its excellent application potential in secure network communication.
An overview of wireless communication technology using deep learning

With the development of 5G, the future wireless communication network tends to be more and more intelligent. In the face of new service demands of communication in the future such as super-heterogeneous network, multiple communication scenarios, large number of antenna elements and large bandwidth, new theories and technologies of intelligent communication have been widely studied, among which Deep Learning (DL) is a powerful technology in artificial intelligence(AI). It can be trained to continuously learn to update the optimal parameters. This paper reviews the latest research progress of DL in intelligent communication, and emphatically introduces five scenarios including Cognitive Radio (CR), Edge Computing (EC), Channel Measurement (CM), End to end Encoder/Decoder (EED) and Visible Light Communication (VLC). The prospect and challenges of further research and development in the future are also discussed.
Decentralized Spectrum Access System: Vision, Challenges, and a Blockchain Solution

Spectrum access system (SAS) is widely considered the de facto solution to coordinating dynamic spectrum sharing (DSS) and protecting incumbent users. The current SAS paradigm prescribed by the FCC for the CBRS band and standardized by the WInnForum follows a centralized service model in that a spectrum user subscribes to a SAS server for spectrum allocation service. This model, however, neither tolerates SAS server failures (crash or Byzantine) nor resists dishonest SAS administrators, leading to serious concerns about SAS system reliability and trustworthiness. This is especially concerning for the evolving DSS land-scape where an increasing number of SAS service providers and heterogeneous user requirements are coming up. To address these challenges, we propose a novel blockchain-based decentralized SAS architecture called BD-SAS that provides SAS services securely and efficiently, without relying on the trust of each individual SAS server for the overall system trustworthiness. In BD-SAS, a global blockchain (G-Chain) is used for spectrum regulatory compliance while smart contract-enabled local blockchains (L-Chains) are instantiated in individual spectrum zones for automating spectrum access assignment per user request. We hope our vision of a decentralized SAS, the BD-SAS architecture, and discussion on future challenges can open up a new direction toward reliable spectrum management in a decentralized manner.
Improving Compositional Generalization with Latent Structure and Data Augmentation

Generic unstructured neural networks have been shown to struggle on out-of-distribution compositional generalization.Compositional data augmentation via example recombination has transferred some prior knowledge about compositionality to such black-box neural models for several semantic parsing tasks, but this often required task-specific engineering or provided limited gains.We present a more powerful data recombination method using a model called Compositional Structure Learner (CSL).CSL is a generative model with a quasi-synchronous context-free grammar backbone, which we induce from the training data.We sample recombined examples from CSL and add them to the fine-tuning data of a pre-trained sequence-tosequence model (T5).This procedure effectively transfers most of CSL's compositional bias to T5 for diagnostic tasks, and results in a model even stronger than a T5-CSL ensemble on two real world compositional generalization tasks.This results in new state-ofthe-art performance for these challenging semantic parsing tasks requiring generalization to both natural language variation and novel compositions of elements.* Equal contribution.† Work done as part of the Google AI Residency program. 1 Also commonly referred to as elements or concepts.
Can NLI Models Verify QA Systems’ Predictions?

To build robust question answering systems, we need the ability to verify whether answers to questions are truly correct, not just "good enough" in the context of imperfect QA datasets.We explore the use of natural language inference (NLI) as a way to achieve this goal, as NLI inherently requires the premise (document context) to contain all necessary information to support the hypothesis (proposed answer to the question).We leverage large pretrained models and recent prior datasets to construct powerful question conversion and decontextualization modules, which can reformulate QA instances as premise-hypothesis pairs with very high reliability.Then, by combining standard NLI datasets with NLI examples automatically derived from QA training data, we can train NLI models to evaluate QA systems' proposed answers.We show that our approach improves the confidence estimation of a QA model across different domains.Careful manual analysis over the predictions of our NLI model shows that it can further identify cases where the QA model produces the right answer for the wrong reason, i.e., when the answer sentence does not address all aspects of the question. Context:The first season of the fantasy comedy television series The Good Place, created by Michael Schur, aired … The series focuses on Eleanor Shellstrop (Kristen Bell) , a woman who wakes up in the afterlife and is introduced by Michael (Ted Danson) to a Heaven-like utopia he designed … Question: Who plays the bad guy in the Good Place?Answer:
CDLM: Cross-Document Language Modeling

We introduce a new pretraining approach geared for multi-document language modeling, incorporating two key ideas into the masked language modeling self-supervised objective.First, instead of considering documents in isolation, we pretrain over sets of multiple related documents, encouraging the model to learn cross-document relationships.Second, we improve over recent long-range transformers by introducing dynamic global attention that has access to the entire input to predict masked tokens.We release CDLM (Cross-Document Language Model), a new general language model for multi-document setting that can be easily applied to downstream tasks.Our extensive analysis shows that both ideas are essential for the success of CDLM, and work in synergy to set new state-of-the-art results for several multi-text tasks.
An adaptive gain based approach for event-triggered state estimation with unknown parameters and sensor nonlinearities over wireless sensor networks

The distributed state and parameter estimation problem is investigated in this paper for discrete-time nonlinear systems subject to sensor nonlinearities and stochastic disturbances over a wireless sensor network. A novel architecture for distributed state estimator is introduced that incorporates adaptive coupling gains to govern the information exchange between the sensor nodes under event-triggering mechanism. The aim of this paper is to provide a scalable structure for unknown parameter identification independent of sensor networks’ complexity. The boundedness of estimation error is ensured in the framework of uniformly ultimately bounded stability by developing an algebraic connectivity based criterion. The estimator gains including proposed coupling gains are then presented as solution to matrix inequalities. Finally, two simulation examples are presented to demonstrate the effectiveness of proposed estimation architecture.
State of the Art of Machine Learning–Enabled Clinical Decision Support in Intensive Care Units: Literature Review

Modern clinical care in intensive care units is full of rich data, and machine learning has great potential to support clinical decision-making. The development of intelligent machine learning-based clinical decision support systems is facing great opportunities and challenges. Clinical decision support systems may directly help clinicians accurately diagnose, predict outcomes, identify risk events, or decide treatments at the point of care.We aimed to review the research and application of machine learning-enabled clinical decision support studies in intensive care units to help clinicians, researchers, developers, and policy makers better understand the advantages and limitations of machine learning-supported diagnosis, outcome prediction, risk event identification, and intensive care unit point-of-care recommendations.We searched papers published in the PubMed database between January 1980 and October 2020. We defined selection criteria to identify papers that focused on machine learning-enabled clinical decision support studies in intensive care units and reviewed the following aspects: research topics, study cohorts, machine learning models, analysis variables, and evaluation metrics.A total of 643 papers were collected, and using our selection criteria, 97 studies were found. Studies were categorized into 4 topics-monitoring, detection, and diagnosis (13/97, 13.4%), early identification of clinical events (32/97, 33.0%), outcome prediction and prognosis assessment (46/97, 47.6%), and treatment decision (6/97, 6.2%). Of the 97 papers, 82 (84.5%) studies used data from adult patients, 9 (9.3%) studies used data from pediatric patients, and 6 (6.2%) studies used data from neonates. We found that 65 (67.0%) studies used data from a single center, and 32 (33.0%) studies used a multicenter data set; 88 (90.7%) studies used supervised learning, 3 (3.1%) studies used unsupervised learning, and 6 (6.2%) studies used reinforcement learning. Clinical variable categories, starting with the most frequently used, were demographic (n=74), laboratory values (n=59), vital signs (n=55), scores (n=48), ventilation parameters (n=43), comorbidities (n=27), medications (n=18), outcome (n=14), fluid balance (n=13), nonmedicine therapy (n=10), symptoms (n=7), and medical history (n=4). The most frequently adopted evaluation metrics for clinical data modeling studies included area under the receiver operating characteristic curve (n=61), sensitivity (n=51), specificity (n=41), accuracy (n=29), and positive predictive value (n=23).Early identification of clinical and outcome prediction and prognosis assessment contributed to approximately 80% of studies included in this review. Using new algorithms to solve intensive care unit clinical problems by developing reinforcement learning, active learning, and time-series analysis methods for clinical decision support will be greater development prospects in the future.
Improvement of phase sensitivity in an SU(1,1) interferometer via a phase shift induced by a Kerr medium

We theoretically study the phase sensitivity of an SU(1,1) interferometer, in which the phase shift is induced by a Kerr medium, together with a coherent state input and homodyne detection. Considering both ideal and photon-loss cases, the results show that compared with the linear-phase-shift-based SU(1,1) interferometer, the Kerr-phase-shift-based SU(1,1) [KSU(1,1)] interferometer can show the significant enhancement of the phase sensitivity and quantum Fisher information even in the presence of photon losses. In particular, without photon losses, the phase sensitivity of the KSU(1,1) interferometer can break through the standard quantum limit and the Heisenberg limit (HL), even close to the super-HL. From the perspective of quantum resource theory, it is interesting that the phase shift induced by the Kerr medium shows an obvious advantage of low-cost input resources to obtain higher phase sensitivity and larger quantum Fisher information. These findings may have potential applications for state-of-the-art quantum information technology.
Forecasting Solar Energy Production Using Machine Learning

When it comes to large-scale renewable energy plants, the future of solar power forecasting is vital to their success. For reliable predictions of solar electricity generation, one must take into consideration changes in weather patterns over time. In this paper, a hybrid model that integrates machine learning and statistical approaches is suggested for predicting future solar energy generation. In order to improve the accuracy of the suggested model, an ensemble of machine learning models was used in this study. The results of the simulation show that the proposed method has reduced placement cost, when compared with existing methods. When comparing the performance of an ensemble model that integrates all of the combination strategies to standard individual models, the suggested ensemble model outperformed the conventional individual models. According to the findings, a hybrid model that made use of both machine learning and statistics outperformed a model that made sole use of machine learning in its performance.