Reviewerly/exHarmony huggingface dataset:
Schools overcoming the digital divide: in depth analyses towards organizational resilience in the computer and information literacy domain

Abstract Background The ongoing digitalization poses new challenges for schools concerning students’ digital skills. In this context, the International Computer and Information Literacy Study (IEA-ICILS 2018) has identified substantial social disparities concerning computer and information literacy of grade 8 students. Furthermore, it has been observed that many schools, especially those located in socioeconomically challenged areas, are particularly engaged in supporting students’ digital literacy and innovatively designing learning processes with information and communications technology (ICT). Empirical studies have made it apparent that some schools have high average achievements concerning domains such as reading literacy and mathematics in spite of socioeconomically challenged student bodies. These schools are regarded as being organizationally resilient. This contribution focusses on these organizationally resilient schools with regard to the domain of computer and information literacy. It aims to investigate how these schools can be classified as a typology according to selected school and/or teacher characteristics. Methods As a first step, representative samples of IEA-ICILS 2018 are used to identify organizationally resilient schools by using students’ achievement measures (plausible values) and their background information regarding their families’ socio-economic status. In order to develop a typology, latent profile analyses (LPA) are conducted by using input and process indicators on the school level, which have been proven to be relevant for implementation through existing empirical research. Results About one twentieth of the schools examined in this contribution can be regarded as organizationally resilient and can be allocated onto a typology of three school types that are characterized by different school-level factors. Conclusions The international comparison permits the conclusion that the phenomenon of organizational resilience is also existent with regard to the content domain of computer and information literacy. However, the proportion of resilient schools is subject to considerable variation between countries.
Transfer Model Collaborating Metric Learning and Dictionary Learning for Cross-Domain Facial Expression Recognition

Facial expression recognition has drawn increasing attention because of its great potential in human behavior analysis. Traditional recognition models usually assume that the training set is sufficient, and the training and testing data sets have the same distribution. However, these two factors are not satisfied in some cases. In this study, a transfer model collaborating metric learning and dictionary learning called TMMLDL is proposed to address the transfer facial expression recognition problem. To reduce the impact of cross-domain distribution variation, the information of global structure and pairwise constraints are utilized among training images in different domains. In particular, a discriminative metric space is learned into dictionary learning procedure such that the dictionary items can well present the discriminative information of different facial expression classes in the metric subspace. The proposed model tunes dictionary and metric space in an alternative optimization algorithm, which is guaranteed to obtain the optimal model parameters simultaneously. The experimental results on nine cross-domain facial expression classification tasks show that the proposed model achieves the satisfactory recognition performance.
Non-Repudiation Storage and Access Control Scheme of Insurance Data Based on Blockchain in IPFS

The insurance business plays a quite significant role in people's lives, but in the process of claim settlement, there are still various frauds such that the insurance companies' refusal to compensate or customers' malicious fraud to obtain compensation. Therefore, it is very important to ensure fair and just claims. In this paper, by combining the blockchain technology and the ciphertext-policy attribute-based encryption system, we build a scheme for secure storage and update for insurance records under the InterPlanetary File System (IPFS) storage environment in the insurance system. In this scheme, we use the fog node to outsource encryption of insurance records to improve the efficiency of the staff; In addition, we store encrypted insurance records on IPFS to ensure the security of the storage platform and avoid the single point failure of the centralized mechanism. In addition, we use the immutability of the blockchain to achieve the non-repudiation of both insurance companies and the client. The security proof shows that the proposed scheme can achieve selective security against selected keyword attacks. Our scheme is efficient and feasible under performance analysis and real data set experiments.
A Deep Reinforcement Learning Approach to the Optimization of Data Center Task Scheduling

With more businesses are running online, the scale of data centers is increasing dramatically. The task-scheduling operation with traditional heuristic algorithms is facing the challenges of uncertainty and complexity of the data center environment. It is urgent to use new technology to optimize the task scheduling to ensure the efficient task execution. This study aimed at building a new scheduling model with deep reinforcement learning algorithm, which integrated the task scheduling with resource-utilization optimization. The proposed scheduling model was trained, tested, and compared with classical scheduling algorithms on real data center datasets in experiments to show the effectiveness and efficiency. The experiment report showed that the proposed algorithm worked better than the compared classical algorithms in the key performance metrics: average delay time of tasks, task distribution in different delay time levels, and task congestion degree.
Popularity Prediction of Instagram Posts

Predicting the popularity of posts on social networks has taken on significant importance in recent years, and several social media management tools now offer solutions to improve and optimize the quality of published content and to enhance the attractiveness of companies and organizations. Scientific research has recently moved in this direction, with the aim of exploiting advanced techniques such as machine learning, deep learning, natural language processing, etc., to support such tools. In light of the above, in this work we aim to address the challenge of predicting the popularity of a future post on Instagram, by defining the problem as a classification task and by proposing an original approach based on Gradient Boosting and feature engineering, which led us to promising experimental results. The proposed approach exploits big data technologies for scalability and efficiency, and it is general enough to be applied to other social media as well.
FILTER: An Enhanced Fusion Method for Cross-lingual Language Understanding

Large-scale cross-lingual language models (LM), such as mBERT, Unicoder and XLM, have achieved great success in cross-lingual representation learning. However, when applied to zero-shot cross-lingual transfer tasks, most existing methods use only single-language input for LM finetuning, without leveraging the intrinsic cross-lingual alignment between different languages that proves essential for multilingual tasks. In this paper, we propose FILTER, an enhanced fusion method that takes cross-lingual data as input for XLM finetuning. Specifically, FILTER first encodes text input in the source language and its translation in the target language independently in the shallow layers, then performs cross-language fusion to extract multilingual knowledge in the intermediate layers, and finally performs further language-specific encoding. During inference, the model makes predictions based on the text input in the target language and its translation in the source language. For simple tasks such as classification, translated text in the target language shares the same label as the source language. However, this shared label becomes less accurate or even unavailable for more complex tasks such as question answering, NER and POS tagging. To tackle this issue, we further propose an additional KL-divergence self-teaching loss for model training, based on auto-generated soft pseudo-labels for translated text in the target language. Extensive experiments demonstrate that FILTER achieves new state of the art on two challenging multilingual multi-task benchmarks, XTREME and XGLUE.
Geostatistical and Remote Sensing Studies to Identify High Metallogenic Potential Regions in the Kivi Area of Iran

The Kivi area in the East Azerbaijan Province of Iran is one of the country’s highest-potential regions for metal element exploration. The primary goal herein was to process the data obtained from geochemical, geostatistical, and remote sensing tools (in the form of stream sediment samples and satellite images) to identify metallic mineralization anomalies in the region. After correcting the raw stream sediment geochemical data, single-variable statistical processing was performed, and Ti and Zn were identified as the elements with the highest degree of contrast. The relationship among these elements was further investigated using correlation and hierarchical clustering analyses. Principal component analysis was then applied to determine the principal components related to these elements, which were subsequently plotted on a regional geological map. Elements related to Ti and Zn were identified using threshold limits of anomalous samples determined via linear discriminant analysis. Lithological units and alteration patterns were detected through remote sensing investigations on Landsat-8 images. Stream sediment geochemical and remote sensing survey results identified anomalous areas of Ti and Zn in the eastern part of the study region. Our results indicate that Ti and Zn are good pathfinder elements for further exploratory investigation in this area.
Human pose estimation based in-home lower body rehabilitation system

In this paper, we design, develop and evaluate an in-home lower body rehabilitation system based on a novel lightweight human pose estimation model. To achieve that, we first create a lower body rehabilitation dataset of 500,000 images with each image annotated with the ground truth joint point locations. The dataset consists of 31 different types of lower body rehabilitation activities from twenty volunteers. After that, we design a lightweight but powerful neural network model, which runs on a smartphone, to estimate human pose. Furthermore, we develop a series of principles for evaluating in-home rehabilitation activities of patients in terms of the range of motion and duration of activities. For the concern of privacy, all the data collected from patients are encrypted, stored and processed locally on patients' own smartphones. Only the sanitized evaluation reports are uploaded and shared with the patients' primary doctors. Our model achieves 70.8 in AP score on the COCO val2017 set with only 4.7M parameters and 1.0 GFLOPs. Using our system, patients can perform lower body rehabilitation activities at home and obtain evaluation report without the presence of physical therapists. We believe our system can greatly facilitate in-home rehabilitation and reduce the cost for patients.
Anonymous Quantum Conference Key Agreement

Conference key agreement (CKA) is a cryptographic effort of multiple parties to establish a shared secret key. In future quantum networks, generating secret keys in an anonymous way is of tremendous importance for parties that want to keep their shared key secret and at the same time protect their own identity. We provide a definition of anonymity for general protocols and present a CKA protocol that is provably anonymous under realistic adversarial scenarios. We base our protocol on shared Greenberger-Horne-Zeilinger states, which have been proposed as more efficient resources for CKA protocols, compared to bipartite entangled resources. The existence of secure and anonymous protocols based on multipartite entangled states provides a new insight on their potential as resources and paves the way for further applications.Received 9 October 2020Accepted 24 November 2020DOI:https://doi.org/10.1103/PRXQuantum.1.020325Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum cryptographyQuantum networksQuantum Information
Incentive Design and Differential Privacy Based Federated Learning: A Mechanism Design Perspective

Due to stricter data management regulations and large size of the training data, distributed learning paradigm such as federated learning (FL) has gained attention recently.FL is capable of significantly preserving end-users' private data from being exposed to external adversaries.However, private information can still be divulged by uploading parameters from users.Therefore, a key challenge in the FL platform is how users participate to build a high-quality learning model with effectively preventing information leakage.To address the above challenge, we design a novel incentive mechanism to attract more data owners to join in the FL process with the consideration of privacy preservation.To implement our proposed scheme, we adopt the concepts of mechanism design (MD) and differential privacy (DP); MD takes an objectives-first approach to designing incentives toward desired objectives, and the DP can provide a theoretical guarantee for users' privacy in FL participations.Based on the DP based incentive mechanism, our joint approach can leverage the full synergy that gives mutual advantages for users and learning operators.Therefore, we can take various benefits in a rational way under the dynamic changing FL environments.Through simulation analysis, the numerical results validate the performance efficiency of our proposed scheme.
Performance analysis of local exit for distributed deep neural networks over cloud and edge computing

In edge computing, most procedures, including data collection, data processing, and service provision, are handled at edge nodes and not in the central cloud. This decreases the processing burden on the central cloud, enabling fast responses to end-device service requests in addition to reducing bandwidth consumption. However, edge nodes have restricted computing, storage, and energy resources to support computation-intensive tasks such as processing deep neural network (DNN) inference. In this study, we analyze the effect of models with single and multiple local exits on DNN inference in an edge-computing environment. Our test results show that a single-exit model performs better with respect to the number of local exited samples, inference accuracy, and inference latency than a multi-exit model at all exit points. These results signify that higher accuracy can be achieved with less computation when a single-exit model is adopted. In edge computing infrastructure, it is therefore more efficient to adopt a DNN model with only one or a few exit points to provide a fast and reliable inference service.
Secure and Verifiable Multikey Image Search in Cloud-Assisted Edge Computing

With the development of outsourcing services, users with limited resources tend to store encrypted images on remote servers and search them anytime and anywhere. However, existing encrypted image search schemes are proposed for cloud computing scenarios, and have some defects, such as excessive bandwidth resource consumption or network delay, which are not suitable for Internet of Things (IoT) devices in edge computing environment. Therefore, we propose a secure and verifiable multikey image search (SVMIS) scheme in cloud-assisted edge computing. First, the pretrained convolutional neural network model is employed to extract image feature vectors to improve search accuracy. Then, a key distribution protocol is designed to convert the encrypted indexes of different owners, and a transformation key list is constructed to support the multikey setting in edge computing. Next, the learning with errors based secure k-nearest neighbor algorithm is used to encrypt feature vectors to improve security. Finally, the Merkle hash tree is utilized to check the correctness of search results returned by edge servers. Theoretical analysis and extensive experiments using a real-world dataset evaluate the security and effectiveness of SVMIS.
BERT-enhanced Relational Sentence Ordering Network

In this paper, we introduce a novel BERTenhanced Relational Sentence Ordering Network (referred to as BERSON) by leveraging BERT for capturing a better dependency relationship among sentences to enhance the coherence modeling for the entire paragraph.In particular, we develop a new Relational Pointer Decoder (referred as RPD) by incorporating the relative ordering information into the pointer network with a Deep Relational Module (referred as DRM), which utilizes BERT to exploit the deep semantic connection and relative ordering between sentences.This enables us to strengthen both local and global dependencies among sentences.Extensive evaluations are conducted on six public datasets.The experimental results demonstrate the effectiveness and promise of BERSON, showing a significant improvement over the state-of-the-art by a wide margin.
Automatically Identifying Gender Issues in Machine Translation using Perturbations

The successful application of neural methods to machine translation has realized huge quality advances for the community.With these improvements, many have noted outstanding challenges, including the modeling and treatment of gendered language.While previous studies have identified issues using synthetic examples, we develop a novel technique to mine examples from real world data to explore challenges for deployed systems.We use our method to compile an evaluation benchmark spanning examples for four languages from three language families, which we publicly release to facilitate research.The examples in our benchmark expose where model representations are gendered, and the unintended consequences these gendered representations can have in downstream application.
Fiji plugin for annotating movies with custom arrows

Annotation of time-lapse data provides an important tool to highlight dynamic processes. Particularly, arrows, circles and arrowheads are useful to pinpoint a specific process, stationary or evolving over time. Here, we describe a user-friendly Fiji plugin to facilitate annotation of movies with arrows, arrowheads and circles. The plugin also enables saving and loading of annotated tracks.
Convolutional Hierarchical Attention Network for Query-Focused Video Summarization

Previous approaches for video summarization mainly concentrate on finding the most diverse and representative visual contents as video summary without considering the user's preference. This paper addresses the task of query-focused video summarization, which takes user's query and a long video as inputs and aims to generate a query-focused video summary. In this paper, we consider the task as a problem of computing similarity between video shots and query. To this end, we propose a method, named Convolutional Hierarchical Attention Network (CHAN), which consists of two parts: feature encoding network and query-relevance computing module. In the encoding network, we employ a convolutional network with local self-attention mechanism and query-aware global attention mechanism to learns visual information of each shot. The encoded features will be sent to query-relevance computing module to generate query-focused video summary. Extensive experiments on the benchmark dataset demonstrate the competitive performance and show the effectiveness of our approach.
An application of survival analysis to disruption prediction via Random Forests

One of the most pressing challenges facing the fusion community is adequately mitigating or, even better, avoiding disruptions of tokamak plasmas. However, before this can be done, disruptions must first be predicted with sufficient warning time to actuate a response. The established field of survival analysis provides a convenient statistical framework for time-to-event (i.e. time-to-disruption) studies. This paper demonstrates the integration of an existing disruption prediction machine learning algorithm with the Kaplan–Meier estimator of survival probability. Specifically discussed are the implied warning times from binary classification of disruption databases and the interpretation of output signals from Random Forest algorithms trained and tested on these databases. This survival analysis approach is applied to both smooth and noisy test data to highlight important features of the survival and hazard functions. In addition, this method is applied to three Alcator C-Mod plasma discharges and compared to a threshold-based scheme for triggering alarms. In one case, both techniques successfully predict the disruption; although, in another, neither warns of the impending disruption with enough time to mitigate. For the final discharge, the survival analysis approach could avoid the false alarm triggered by the threshold method. Limitations of this analysis and opportunities for future work are also presented.
Semi-Supervised Cross-Modal Retrieval With Label Prediction

Due to abundance of data from multiple modalities, cross-modal retrieval tasks with image-text, audio-image, etc. are gaining increasing importance.Of the different approaches proposed, supervised methods usually give significant improvement over their unsupervised counterparts at the additional cost of labeling or annotation of the training data.Semi-supervised methods are recently becoming popular as they provide an elegant framework to balance the conflicting requirement of labeling cost and accuracy.In this work, we propose a novel deep semi-supervised framework which can seamlessly handle both labeled as well as unlabeled data.The network has two important components: (a) the label prediction component predicts the labels for the unlabeled portion of the data and then (b) a common modality-invariant representation is learned for cross-modal retrieval.The two parts of the network are trained sequentially one after the other.Extensive experiments on three standard benchmark datasets, Wiki, Pascal VOC and NUS-WIDE demonstrate that the proposed framework outperforms the state-of-the-art for both supervised and semi-supervised settings.