Reviewerly/exHarmony huggingface dataset:
An extended attention mechanism for scene text recognition

Scene text recognition (STR) refers to obtaining text information from natural text images. The task is more challenging than the optical character recognition(OCR) due to the variability of scenes. Attention mechanism, which assigns different weights to each feature vector at each time step, guides the text recognition decoding process. However, when the given query and the key/value are not related, the generated attention result will contain irrelevant information, which could lead the model to give wrong results. In this paper, we propose an extended attention-based framework for STR tasks. In particular, we have integrated an extended attention mechanism named Attention on Attention (AoA), which is able to determine the relevance between attention results and queries, into both the encoder and the decoder of a common text recognition framework. By two separate linear functions, the AoA module generates an information vector and an attention gate using the attention result and the current context. Then AoA adds new attention by applying element-wise multiplication to acquire final attended information. Our method is compared with seven benchmarks over eight datasets. Experimental results show that our method outperforms all the seven benchmarks, by 6.7% and 1.4% than the worst and best works on average.
Blockchain-Enabled: Multi-Layered Security Federated Learning Platform for Preserving Data Privacy

Privacy and data security have become the new hot topic for regulators in recent years. As a result, Federated Learning (FL) (also called collaborative learning) has emerged as a new training paradigm that allows multiple, geographically distributed nodes to learn a Deep Learning (DL) model together without sharing their data. Blockchain is becoming a new trend as data protection and privacy are concerns in many sectors. Technology is leading the world and transforming into a global village where everything is accessible and transparent. We have presented a blockchain enabled security model using FL that can generate an enhanced DL model without sharing data and improve privacy through higher security and access rights to data. However, existing FL approaches also have unique security vulnerabilities that malicious actors can exploit and compromise the trained model. The FL method is compared to the other known approaches. Users are more likely to choose the latter option, i.e., providing local but private data to the server and using ML apps, performing ML operations on the devices without benefiting from other users’ data, and preventing direct access to raw data and local training of ML models. FL protects data privacy and reduces data transfer overhead by storing raw data on devices and combining locally computed model updates. We have investigated the feasibility of data and model poisoning attacks under a blockchain-enabled FL system built alongside the Ethereum network and the traditional FL system (without blockchain). This work fills a knowledge gap by proposing a transparent incentive mechanism that can encourage good behavior among participating decentralized nodes and avoid common problems and provides knowledge for the FL security literature by investigating current FL systems.
Deep Active Learning Framework for Lymph Node Metastasis Prediction in Medical Support System

Assessing the extent of cancer spread by histopathological analysis of sentinel axillary lymph nodes is an important part of breast cancer staging. With the maturity and prevalence of deep learning technology, building auxiliary medical systems can help to relieve the burden of pathologists and increase the diagnostic precision and accuracy during this process. However, such histopathological images have complex patterns that are difficult for ordinary people to understand and require professional medical practitioners to annotate. This increases the cost of constructing such medical systems. To reduce the cost of annotating and improve the performance of the model as much as possible, in other words, using as few labeled samples as possible to obtain a greater performance improvement, we propose a deep learning framework with a three-stage query strategy and novel model update strategy. The framework first trains an auto-encoder with all the samples to obtain a global representation in a low-dimensional space. In the query stage, the unlabeled samples are first selected according to uncertainty, and then, coreset-based methods are employed to reduce sample redundancy. Finally, distribution differences between labeled samples and unlabeled samples are evaluated and samples that can quickly eliminate the distribution differences are selected. This method achieves faster iterative efficiency than the uncertainty strategies, representative strategies, or hybrid strategies on the lymph node slice dataset and other commonly used datasets. It reaches the performance of training with all data, but only uses 50% of the labeled. During the model update process, we randomly freeze some weights and only train the task model on new labeled samples with a smaller learning rate. Compared with fine-tuning task model on new samples, large-scale performance degradation is avoided. Compared with the retraining strategy or the replay strategy, it reduces the training cost of updating the task model by 79.87% and 90.07%, respectively.
Slow improvement to the archiving quality of open datasets shared by researchers in ecology and evolution

Many leading journals in ecology and evolution now mandate open data upon publication. Yet, there is very little oversight to ensure the completeness and reusability of archived datasets, and we currently have a poor understanding of the factors associated with high-quality data sharing. We assessed 362 open datasets linked to first- or senior-authored papers published by 100 principal investigators (PIs) in the fields of ecology and evolution over a period of 7 years to identify predictors of data completeness and reusability (data archiving quality). Datasets scored low on these metrics: 56.4% were complete and 45.9% were reusable. Data reusability, but not completeness, was slightly higher for more recently archived datasets and PIs with less seniority. Journal open data policy, PI gender and PI corresponding author status were unrelated to data archiving quality. However, PI identity explained a large proportion of the variance in data completeness (27.8%) and reusability (22.0%), indicating consistent inter-individual differences in data sharing practices by PIs across time and contexts. Several PIs consistently shared data of either high or low archiving quality, but most PIs were inconsistent in how well they shared. One explanation for the high intra-individual variation we observed is that PIs often conduct research through students and postdoctoral researchers, who may be responsible for the data collection, curation and archiving. Levels of data literacy vary among trainees and PIs may not regularly perform quality control over archived files. Our findings suggest that research data management training and culture within a PI's group are likely to be more important determinants of data archiving quality than other factors such as a journal's open data policy. Greater incentives and training for individual researchers at all career stages could improve data sharing practices and enhance data transparency and reusability.
A privacy-preserving content-based image retrieval method based on deep learning in cloud computing

Privacy-preserving Content-Based Image Retrieval (CBIR) method is a promising technology to achieve data confidentiality and searchability in cloud-assisted multimedia (i.e., image or video) data environment. However, inappropriate feature-preserving mechanisms and inefficient ciphertext descriptors resulted in lower performance than expected. Therefore, how to design encryption techniques with high security and how to extract effective features from ciphertext images still hinder privacy-preserving CBIR. For this goal, we propose a privacy-preserving image retrieval based on deep convolutional network features. First, a novel hybrid encryption technique is designed to encrypt images and an improved DenseNet model is fine-tuned by using the encrypted images to construct a feature extractor. The encrypted images and fine-tuning feature extractor are then uploaded to cloud server. Meanwhile, secure CBIR service is executed in the cloud server. We conduct experiments on two public benchmark datasets for performance evaluation in terms of mAP and accuracy. As demonstrated in the experimental results, the proposed method can achieve superior result compared with the existing methods, improving the performance on the two metrics by relatively 1.9% and 10%, respectively. Furthermore, the computational cost and parameters of depthwise separable convolution adopted by the improved DenseNet model are 8 to 9 times smaller than that of standard convolutions of the original DenseNet at only a small reduction in accuracy.
Blockchain-enabled traceability and producer’s incentive to outsource delivery

Applications of the blockchain technology in supply chains have attracted extensive attention in both academia and industries. However, little research has investigated the effects of the blockchain technology on firms’ operational strategies. In this paper, we investigate the impacts of the traceability enabled by the blockchain technology on a producer’s decision whether to outsource delivery to a third-party logistics firm. We find that without the blockchain technology, the logistics firm has a moral hazard—an incentive to set the delivery quality at the lowest level even though improving the delivery quality is for free. The traceability enabled by the blockchain technology can resolve the logistics firm’s moral hazard and encourage the producer to improve the production quality. Furthermore, when the delivery is cost-efficient for the producer, the traceability enabled by the blockchain technology motivates the producer to outsource the delivery to the logistics firm; otherwise, the producer’s outsourcing decision is not affected. When quality cost is low, the blockchain technology makes the logistics firm improve its delivery quality significantly and encourages the producer to improve the production quality. When the quality cost is moderate, only under certain conditions, the blockchain technology has effects on both parties’ decisions.
Implementing Digital Competencies in University Science Education Seminars Following the DiKoLAN Framework

Prospective teachers must acquire subject-specific digital competencies to design contemporary lessons and to promote digital competencies among students themselves. The DiKoLAN framework (Digital Competencies for Teaching in Science Education) describes basic digital competencies for the teaching profession in the natural sciences precisely for this purpose. In this article, we describe the development, implementation, and evaluation of a university course based on DiKoLAN which promotes the digital competencies of science teachers. As an example, the learning module Data Processing in Science Education is presented, and its effectiveness is investigated. For this purpose, we used a questionnaire developed by the Working Group Digital Core Competencies to measure self-efficacy, which can also be used in the future to promote digital competencies among pre-service teachers. The course evaluation showed a positive increase in the students’ self-efficacy expectations. Overall, the paper thus contributes to teacher education by using the course as a best-practice example—a blueprint for designing new courses and for implementing a test instrument for a valid evaluation.
Didactic Use of Virtual Reality in Colombian Universities: Professors’ Perspective

This paper presents quantitative research on the perception of the didactic use of virtual reality by university professors in Colombia, with special attention to the differences according to their area of knowledge, as the main variable, and gender and digital generation, as secondary variables. The study involved 204 professors from different Colombian universities. As an instrument, a survey designed for this purpose was used with four scales that were used to measure, on a Likert scale, different dimensions involving the participants’ perception of the use of virtual reality in the classroom. The answers were analyzed statistically and the differences in the perceptions have been identified by means of parametric statistical tests according to the following: (i) area of knowledge, (ii) gender, (iii) digital generation of the participants. The results showed that the participants expressed high valuations of virtual reality, despite having intermediate or low levels of digital competence. Gaps were identified in terms of area of knowledge, gender, and digital generation (digital natives or immigrants) with respect to opinions of virtual reality and digital competence. The highest valuations of virtual reality are given by professors of Humanities, and by digital natives. It is suggested that Colombian universities implement training plans on digital competence for professors and that these plans be aimed at strengthening knowledge of virtual reality.
Facilitating interorganizational trust in strategic alliances by leveraging blockchain-based systems: Case studies of two eastern banks

Strategic alliances require mutual trust and collaboration between organizations.To increase alliance performance, organizations in a strategic alliance are more likely to interpret each other's equivocal behaviors in a positive way to sustain the stability of the relationship.Different from traditional mechanisms to build interorganizational trust, blockchain facilitates trust among alliance partners via its technology advantages, for example, public ledger and distributed consensus.The decentralized and distributed nature of a blockchainbased system assures that no single organization can add information to the chain without an approval from the alliance, nor can it make unilateral changes in the system.Drawing from the Affordance Theory, we propose an exploratory research design to understand the structure of a blockchain-based system, as well as how this blockchain-based system influences interorganizational trust in a strategic alliance and how blockchain-enabled interorganizational trust influences alliance performance.The case studies of two eastern banks indicate that the technical features of blockchain can help reduce equivocal behaviors and improve interorganizational trust.We discuss the findings and conclude the paper with implications.
Operational safety assessment of straddle-type monorail vehicle system based on cloud model and improved CRITIC method

This paper proposes an improved critic method and establishes an operational safety assessment method for straddle-type monorail vehicle system based on cloud model and improved critic method. Firstly, according to the structure and function of straddle- type monorail vehicle system, the reliability index system of straddle-type monorail vehicle is established, and the weight value of reliability evaluation index is determined by improved critic method. Then, the cloud model expression of reliability evaluation index of straddle-type monorail vehicle system is obtained by virtual cloud. finally, similarity analysis is conducted between the new cloud of straddle-type monorail vehicle system reliability comprehensive index and the cloud model of reliability evaluation set, so as to judge that the comprehensive cloud model of straddle-type monorail vehicle system can be used to evaluate the operation status of “main functions” of the system. The analysis shows that the reliability evaluation method of straddle-type monorail vehicle system based on cloud model and improved critic method can correctly describe the daily operation of straddle-type monorail vehicle system in Chongqing, which provides a method reference for reliability evaluation of straddle-type monorail vehicle system.
Multi-target Pedestrian Tracking Based on YOLOv5 and DeepSORT

Target detection and tracking are crucial in autonomous driving and intelligent transportation, especially in pedestrians and traffic safety. The dynamic pedestrian tracking algorithm using YOLOv5 and DeepSORT is proposed to improve accuracy and robustness, based on the classical tracking-by-detection approach, to achieve real-time monitoring and tracking of pedestrians in the video. The YOLOv5 algorithm is used for pedestrian detection in the video. The YOLOv5 algorithm is used to detect the pedestrian targets in the video and calculate the pedestrian distance to assess the risk. The detected pedestrian features are tracked in real-time using the DeepSORT algorithm to complete the pedestrian trajectory tracking. The experimental results show that the method is effective in detecting pedestrians when coping with the problems of abrupt and rapid movement, occlusion, and appearance deformations, which is suitable for online real-time tracking of pedestrians.
The cost of photovoltaics: Re-evaluating grid parity for PV systems in China

The price of photovoltaics (PV) has been steadily decreasing over the last decade, and many reports suggest that PV has become considerably cheaper than conventional electricity sources. In this paper, we critically evaluate the PV grid parity and use China as a case study. China is an interesting case study due to the wealth of data combined with the recent decrease in financial subsidies. Electricity costs are commonly compared in the literature using levelized costs of electricity (LCOE). However traditional LCOE analyses neglect important cost factors that are specific to PV, in particular the cost of grid integration. Here, we demonstrate that system LCOE calculation more accurately estimates the grid parity of PV. We find that the integration costs account for 15% of the total system costs, which cannot be neglected with the higher penetration of PV in the electricity system. Further, provincial grid parity indexes are greater than 1, indicating that the grid parity from a system LCOE perspective has not yet been achieved. The learning rate of Chinese PV is still relatively small, and we estimate that grid parity for PV in China will be achieved between 2020 and 2032, depending on the region. These findings shed new light on assessing grid parity of PV systems by considering relevant local parameters and thereby offer an assessment method framework and prediction modeling for assessing grid parity of PV installations.
Comparison of Webcam and Remote Eye Tracking

We compare the measurement error and validity of webcam-based eye tracking to that of a remote eye tracker as well as software integration of both. We ran a study with n = 83 participants, consisting of a point detection task and an emotional visual search task under three between-subjects experimental conditions (webcam-based, remote, and integrated). We analyzed location-based (e.g., fixations) and process-based eye tracking metrics (ambient-focal attention dynamics). Despite higher measurement error of webcam eye tracking, our results in all three experimental conditions were in line with theoretical expectations. For example, time to first fixation toward happy faces was significantly shorter than toward sad faces (the happiness-superiority effect). As expected, we also observed the switch from ambient to focal attention depending on complexity of the visual stimuli. We conclude that webcam-based eye tracking is a viable, low-cost alternative to remote eye tracking.
Enhance VR: A Multisensory Approach to Cognitive Training and Monitoring

Cognitive training systems aim to improve specific domains or global cognition by engaging users in cognitively demanding tasks. While screen-based applications can improve performance in the trained cognitive abilities, they are often criticized for their poor transferability to activities of daily living. These systems, however, exclude the user's body and motor skills, which invariably serves to restrict the user experience. Immersive Virtual Reality (IVR) systems, in contrast, present the user with body-related information, such as proprioceptive and visuomotor information, allowing for an immersive and embodied experience of the environment. This feature renders VR a very appealing tool for cognitive training and neurorehabilitation applications. We present Enhance VR, an IVR-based cognitive training and monitoring application that offers short daily cognitive workouts. The games are designed to train and monitor specific cognitive domains such as memory, task flexibility, information processing, orientation, attention, problem-solving, and motor control. The aim is to test whether cognitively demanding tasks, presented in an IVR setting, provide a naturalistic system to train and monitor cognitive capabilities.
Engaging Students With a Chatbot-Based Academic Advising System

Advising systems automate aspects of academic advising. Traditionally, advising systems focused on specialized tasks such as course selection. Recently, chatbot-based advising systems have emerged as they emulate scenario-based advising. Nevertheless, the design of most chatbot-based advising systems is not user-centric, potentially causing a lack of adoption. Further, there is a lack of studies reporting findings of usability evaluation of chatbot-based advising systems. In response, we contribute a chatbot-based academic advising system, MyAdvisor, that helps students with prescriptive academic inquiries. The system is based on real advising scenarios and designed with usability principles. The results show that students learn the system fast and find it helpful. This work contributes (1) scenario-based functional requirements and usability requirements of the chatbot-based advising system, (2) the application of usability heuristics in the design of the system, and (3) findings of empirically evaluating the system usability.
Cross-Scale Edge Purification Network for salient object detection of steel defect images

Salient object detection has achieved great success in natural scene images, but there is big room for exploration in steel defect images (SDIs). Moreover, the unique characteristic of SDIs makes salient object detection in SDIs (SDI-SOD) a challenging task, and many representative methods for natural scene images struggle to get satisfactory results in SDI-SOD. Existing SDI-SOD methods usually ignore the edge information, and focus only on enhancing the feature interaction of different layers. To this end, we propose a specialized Cross-Scale Edge Purification Network (CSEPNet) to explore the correlations of features at different scales for SDI-SOD. To be specific, our CSEPNet is based on the general encoder–decoder architecture. First, we adopt a generic Convolutional Block Attention Module (CBAM) to refine and enhance the features from the encoder. Then, we propose the Cross-Scale Calibration Module (CSCM) and Cross-Scale Feature Interweaving Module (CSFIM) to capture the relationship of inter-layer features and intra-layer features, respectively. In CSCM, adjacent features of different scales are effectively calibrated by each other and are re-calibrated by their collective weight map to generate informative features. In CSFIM, two branches of features interweave and fuse with their corresponding edge information purified to generate robust features. Besides, we employ deep supervision and use a hybrid loss to guide the training process. We perform comprehensive experiments on the public SD-saliency-900 dataset, and demonstrate that our method is superior to 26 state-of-the-art methods, including both traditional and CNN-based ones. The code and results of our method are available at https://github.com/showmaker369/CSEPNet.