Reviewerly/exHarmony huggingface dataset:
Topic scene graphs for image captioning

When describing an image, people can rapidly extract the topic from the image and find the main object, generating sentences that match the main idea of the image. However, most of the scene graph generation methods do not emphasise the importance of the topic of the image. Consequently, the captions generated by the scene graph-based image captioning models cannot reflect the topic in the image then expressing the central idea of the image. In this paper, we propose a method for image captioning based on topic scene graphs (TSG). Firstly, we propose the structure of topic scene graphs that express images' topics and the relationships between objects. Then, combined with the topic scene graph, we utilise the salient object detection to generate the topic scene graph highlighting the salient objects of the image. Note that our framework is agnostic to any scene graph-based image captioning model and thus can be widely applied in the community which seeks salient object predictions. We compare the performance of our topic scene graph with the state-of-the-art scene graph generation models and mainstream image captioning models on MSCOCO and Visual Genome datasets, both achieving better performance.
Coherence properties of a spin in a squeezed resonator

A promising venue for hybrid quantum computation involves the strong coupling between impurity spins and superconducting resonators. One strategy to control and enhance this coupling is to prepare the resonator in a non-classical state, such as a squeezed state. In this work, we theoretically study the effects of these states on the coherence properties of the spin. We develop an analytic approach based on the Schrieffer-Wolf transformation that allows us to quantitatively predict the coupling and the dephasing rate of the spin, and we numerically confirm its validity. We find that squeezing can enhance the coupling between the resonator and the spin. However, at the same time, it amplifies the photon noise and enhances the spin decoherence. Our work demonstrates a major impediment in using squeezing to reach the strong-coupling limit.
Graph Embedding with Hierarchical Attentive Membership

This paper studies a remarkable property of graphs which is the latent hierarchical grouping of nodes, where each node manifests its membership to a specific group based on the context composed by its neighboring nodes. When modeling the neighborhood structure for graph representation learning, most prior works ignore such latent groups and nodes' membership to different groups, not to mention the hierarchy. Thus, they fall short of delivering a comprehensive understanding of the nodes under different contexts in a graph. In this paper, we propose a novel hierarchical attentive membership model for graph embedding, where the latent memberships for each node are dynamically discovered based on its neighboring context. Both group-level and individual-level attentions are performed when aggregating neighboring states to generate node embeddings. We introduce structural constraints to explicitly regularize the inferred memberships of each node, such that a well-defined hierarchical grouping structure is captured. The proposed model outperformed a set of state-of-the-art graph embedding solutions on node classification and link prediction tasks in a variety of graphs including citation networks and social networks. Qualitative evaluations visualize the learned node embeddings along with the inferred memberships, which proved the concept of membership hierarchy and enables explainable embedding learning in graphs.
A study of the effects of mobile media on L2 text processing: Beyond offline comprehension accuracy measures

Recent research on the processing effects of mobile media has incorporated offline research methods to demonstrate that reading on mobile media is as effective as reading on computers or paper in accuracy of text comprehension. The present study uses an eye-tracking methodology to compare the effects of reading on mobile media (mobile phone and tablet) and traditional media (paper and computer) on cognitive processing behaviours as well as on accuracy. Chinese L2 learners of English (n = 156) read an English novel, The Elephant Man, in one of the four reading conditions (mobile phone: 41, tablet: 39, computer: 38, and paper: 38) while their eye movements were recorded. Between-condition comparisons showed that mobile-assisted reading performance was equally accurate, fast and efficient to process texts, and fast to respond to posttests. Statistically significantly shorter mean fixation duration of mobile-assisted than computer-assisted reading suggests slightly more efficiency in cognitive processing. The overall findings of the present study add to other strong evidence that mobile-assisted reading is effective in text processing. We discuss the implications of our results and provide directions for future research and insights into mobile-assisted language learning, including online learning, e.g., during the COVID-19 global pandemic.
Assessing the effect of human factors in healthcare cyber security practice: An empirical study

In recent times, the human element plays a major role in the surge of data breaches in healthcare. As a result, we hypothesized that there are gaps between the information security (IS) practices of the healthcare staff and the security policies. We further opined that if there are gaps, the causes could be culminating from poor work factors, personality, psychological, social and cultural perception issues. As a result, a survey was conducted with three departments in a typical hospital to assess a broad range of these factors. From the study results, the healthcare staff’ IS knowledge and their self reported IS conscious care behaviour (ISCCB) showed a gap in their security practices. About half of the respondents’ responses were in a higher risk region. In exploring for the reasons for these gaps, Workload showed a negative significant correlation (r=-0.346, p-value= 0.05) with information security-conscious care behaviour(ISCCB) risk and perceived barrier risk (r=-.363, pvalue=0.05). But, the hospital information security culture showed negative significant correlation with perceived vulnerability (r=-0.316, pvalue= 0.05), cues to action (r=-0.508,pvalue=0.01), and punishment severity (r=-0.425, pvalue=0.05). Based on the findings of this study, some intrinsic and extrinsic motivational factors can be explored to improve the security perceptions, and security culture of the hospital if causality is established.
Innovative and Integrated Contact Tracing: Indian Health Service, Arizona, December 2020–January 2021

SARS-CoV-2 has exposed limitations of public health mitigation measures such as traditional case investigations and contact tracing. The Whiteriver Service Unit is a rural, acute care hospital on the Fort Apache Indian Reservation in Arizona with integrated health care delivery and public health services. During the first wave of COVID-19 cases in May-June 2020, we developed an innovative case investigation contact tracing approach that relied heavily on cross-trained personnel, in-person encounters, and baseline clinical evaluations. A second COVID-19 surge during December 13, 2020-January 31, 2021, caused incidence to peak at 413 cases per 100 000 community members. During that second surge, we investigated all 769 newly identified COVID-19 cases and notified 1911 (99.4%) of 1922 reported contacts. Median time interval from nasopharyngeal specimen collection to both case investigation and contact notification was 0 days (range, 0-5 days and 0-13 days, respectively). Our primary lesson was the importance of cross-trained personnel who integrated tasks along the testing-tracing continuum (eg, in-person interviews, prompt referral for additional testing and evaluation). These successive steps fed forward to identify new cases and their respective contacts. Our innovative community-based approach was both successful and efficient; our experience suggests that when adapted based on local needs, case investigation and contact tracing remain valuable and feasible public health tools, even in rural, resource-limited settings.
Path Planning for Mobile Robots Based on an Improved Ant Colony Algorithm with Gaussian Distribution

Abstract Optimal path planning for mobile robots in complex environments has the problem of tangential collision with obstacles. This paper proposed an ant colony algorithm based on Gaussian distributed pheromone volatile mechanism (GD-ACO). The algorithm can reduce the collision probability with obstacles during the search navigation, and it increased the search efficiency and avoidance capability of mobile robot in complex environment path planning. Firstly, we established a grid environment model and introduced a Gaussian distribution pheromone volatility mechanism to perform optimal path planning. Secondly, we introduced right triangle constraints to eliminate the unqualified paths and reduce the complexity of the algorithm through a pheromone reward mechanism. Finally, by improving the heuristic information function distribution of the ant colony algorithm, it solved the algorithm cannot effectively converge to the optimal solution when encountering special obstacle distribution and achieved more efficient obstacle avoidance. Compared with other path planning algorithms, it can effectively generate a collision-free path. The simulation experiments show the improved ant colony algorithm has better obstacle avoidance capability and faster performance, and the proposed method can effectively find the optimal collision-free path through experimental verification.
Learning From the Past to Improve the Future

Abstract Contact tracing apps were considered among the first tools to control the spread of COVID-19 and ease lockdown measures. While these apps can be very effective at stopping transmission and saving lives, the level of adoption remains significantly below the expected critical mass. The public debate as well as academic research about contact tracing apps emphasizes general concerns about privacy (and the associated risks) but often disregards the value-added services, as well as benefits, that can result from a larger user base. To address this gap, the study analyzes goal-congruent features as drivers for user adoption. It uses market research techniques – specifically, conjoint analysis – to study individual and group preferences and gain insights into the prescriptive design. While the results confirm the privacy-preserving design of most European contact tracing apps, they emphasize the role of value-added services in addressing heterogeneous user segments to drive user adoption. The findings thereby are of relevance for designing effective contact tracing apps, but also inform the user-oriented design of apps for health and crisis management that rely on sharing sensitive information.
Coordinating Fast Concurrency Adapting With Autoscaling for SLO-Oriented Web Applications

Cloud providers tend to support dynamic computing resources reallocation (e.g., Autoscaling) to handle the bursty workload for web applications (e.g., e-commerce) in the cloud environment. Nevertheless, we demonstrate that directly scaling a bottleneck server without quickly adjusting its soft resources (e.g., server threads and database connections) can cause significant response time fluctuations of the target web application. Since soft resources determine the request processing concurrency of each server in the system, simply scaling out/in the bottleneck service can unintentionally change the concurrency level of related services, inducing either under- or over-utilization of the critical hardware resource. In this paper, we propose the Scatter-Concurrency-Throughput (SCT) model, which can rapidly identify the near-optimal soft resource allocation of each server in the system using the measurement of each server’s real-time throughput and concurrency. Furthermore, we implement a Concurrency-aware autoScaling (ConScale) framework that integrates the SCT model to quickly reallocate the soft resources of the key servers in the system to best utilize the new hardware resource capacity after the system scaling. Based on extensive experimental comparisons with two widely used hardware-only scaling mechanisms for web applications: EC2-AutoScaling (VM-based autoscaler) and Kubernetes HPA (container-based autoscaler), we show that ConScale can successfully mitigate the response time fluctuations over the system scaling phase in both VM-based and container-based environments.
A Two-Stage CNN Based Channel Estimation for OFDM System

The knowledge regarding the wireless channel parameters is essential for ensuring reliable end-to-end communication. Therefore, correct channel estimation becomes essential to recover the transmitted message efficiently. In this paper, we have proposed a Convolutional Neural Network (CNN) based architecture to estimate the channel using the knowledge regarding pilot locations in the time and frequency domain. We have considered the time-frequency response of the pilot positions as an image and used deep learning (DL) based image processing techniques to recover the complete information for correct channel estimation. The quality of the image is enhanced by a CNN based super-resolution network. Then the output of the first CNN is passed through an image restoration network to have high resolution image in a pipe-lined architecture. Moreover, the performance of the proposed DL based channel estimation has been evaluated in terms of mean square error. It can be observed from the simulation results that the proposed two-stage CNN based approach outperforms other baseline channel estimation methods.
Optimized Federated Learning on Class-Biased Distributed Data Sources

Due to privacy protection, the conventional machine learning approaches, which upload all data to a central location, has become less feasible. Federated learning, a privacy-preserving distributed machine learning paradigm, has been proposed as a solution to comply with privacy requirements. By enabling multiple clients collaboratively to learn a shared global model, model parameters instead of local private data will be exchanged under privacy restrictions. However, compared with centralized approaches, federated learning suffers from performance degradation when trained on non-independently and identically distributed (non-i.i.d.) data across the participants. Meanwhile, the class imbalance problem is always encountered in machine learning in practice and causes bad prediction on minority classes. In this work, We propose FedBGVS to alleviate the class bias severity by employing a balanced global validation set. The model aggregation algorithm is refined by using the Balanced Global Validation Score (BGVS). We evaluate our methods by experiments conducted on both the classical benchmark datasets MNIST, SVHN and CIFAR-10 and a public clinical dataset ISIC-2019. The empirical results demonstrate that our proposed methods outperform the state-of-the-art federated learning algorithms in label distribution skew and class imbalance settings.
Enhancing Knowledge of Propagation-Perception-Based Attention Recommender Systems

Researchers have introduced side information such as social networks or knowledge graphs to alleviate the problems of data sparsity and cold starts in recommendation systems. However, most of the methods ignore the exploration of feature differentiation aspects in the knowledge propagation process. To solve the above problem, we propose a new attention recommendation method based on an enhanced knowledge propagation perception. Specifically, to capture user preferences in a fine-grained manner in a knowledge graph, an asymmetric semantic attention mechanism is adopted. It identifies the influence of propagation neighbors on user preferences through a more precise representation of the preference semantics for head and tail entities. Furthermore, in consideration of the memory and generalization of different propagation depth features and adaptively adjusting the propagation weights, a new propagation feature exploration framework is designed. The performance of the proposed model is validated by two real-world datasets. The baseline model averagely increases by 9.65% and 9.15% for the Area Under Curve (AUC) and Accuracy (ACC) indicators, which proves the effectiveness of the model.
Keypoint-Aware Single-Stage 3D Object Detector for Autonomous Driving

Current single-stage 3D object detectors often use predefined single points of feature maps to generate confidence scores. However, the point feature not only lacks the boundaries and inner features but also does not establish an explicit association between regression box and confidence scores. In this paper, we present a novel single-stage object detector called keypoint-aware single-stage 3D object detector (KASSD). First, we design a lightweight location attention module (LLM), including feature reuse strategy (FRS) and location attention module (LAM). The FRS can facilitate the flow of spatial information. By considering the location, the LAM adopts weighted feature fusion to obtain efficient multi-level feature representation. To alleviate the inconsistencies mentioned above, we introduce a keypoint-aware module (KAM). The KAM can model spatial relationships and learn rich semantic information by representing the predicted object as a set of keypoints. We conduct experiments on the KITTI dataset. The experimental results show that our method has a competitive performance with 79.74% AP on a moderate difficulty level while maintaining 21.8 FPS inference speed.
Tailor Me: An Editing Network for Fashion Attribute Shape Manipulation

Fashion attribute editing aims to manipulate fashion images based on a user-specified attribute, while preserving the details of the original image as intact as possible. Recent works in this domain have mainly focused on direct manipulation of the raw RGB pixels, which only allows to perform edits involving relatively small shape changes (e.g., sleeves). The goal of our Virtual Personal Tailoring Network (VPTNet) is to extend the editing capabilities to much larger shape changes of fashion items, such as cloth length. To achieve this goal, we decouple the fashion attribute editing task into two conditional stages: shape-then-appearance editing. To this aim, we propose a shape editing network that employs a semantic parsing of the fashion image as an interface for manipulation. Compared to operating on the raw RGB image, our parsing map editing enables performing more complex shape editing operations. Second, we introduce an appearance completion network that takes the previous stage results and completes the shape difference regions to produce the final RGB image. Qualitative and quantitative experiments on the DeepFashion-Synthesis dataset confirm that VPTNet outperforms state-of-the-art methods for both small and large shape attribute editing.
Public administration and new venture startups: The association between economic development and the role of bureaucracy in startup activity

Using public administration theory, we examine whether professionalized or closed bureaucracy are associated with individual participation in startups and whether these associations are contingent on the level of economic development of a country. We use a novel cross-national 2015 dataset of bureaucratic structures and a sample of 106,818 individuals from 22 countries from the Global Entrepreneurship Monitor survey. Our findings indicate that both closed and professional public administrations lower the odds of being in a startup. Furthermore, we find that the underlying economic conditions within countries can moderate these relationships. Specifically, our results indicate that the negative relationship between both closed and professional public administrations and the odds of being in a startup is amplified in low-income countries.
Credit Payment Fraud detection model based on TabNet and Xgboot

Nowadays, people tend to use credit cards for their payment efficiency, but credit cards also provide a new opportunity for fraud. Enterprises and researchers have been trying to come up with a method to indicate whether a transaction is fraudulent. In this article, a hybrid model based on the combination of TabNet and XGBoost is proposed. A dataset provided by IEEE-CIS, which contains many transaction records and whether they are fraudulent, is used in this research. The dataset is compressed in the feature engineering process, and the missing values are replaced with −999. In the training process, the cross-validation is used to split the dataset into five-folds then trained one by one. Besides, some improvements are made to cut down the memory usage. Finally, a comparison between TabNet model and two classical models, Naïve Bayes and XGBOOST are made, to evaluate the performance of TabNet. The result shows that our hybrid model has the best performance in terms of two metrics‐‐‐‐accuracy and AUC-ROC score‐‐‐‐among all models tested. TabNet has the highest AUC-ROC score and accuracy, 0.892 and 0.972, respectively.
CRchain: An Efficient Certificate Revocation Scheme Based on Blockchain

One of essential parts of public key infrastructure is the ability to efficiently check the certificate status and quickly distribute certificates revocation information. The existing certificate revocation schemes generally suffer from a time-consuming process of certificate status verification as well as a long interval of revocation information updating. To address aforementioned issues, this paper proposes a blockchain-based certificate revocation mechanism, namely CRchain, which can efficiently check certificate status and revoke the certificate. Specifically, to achieve efficient certificate status queries, revokedCertCF and validCertCF cuckoo filters are constructed for storing the revoked and valid certificates, respectively. And then, the co-controlled key published on CRchain is presented for shortening the certificate revocation process and abridging the authority of certificate authorities. Finally, we implement and evaluate CRchain on Hyperledger Fabric with smart contract. The theoretical analysis and experimental results show that CRchain achieves better performance in latency and exchanged data size than existing reference methods in the duration of the certificate status checking.