Reviewerly/exHarmony huggingface dataset:
Six Attributes of Unhealthy Conversations

We present a new dataset of approximately 44000 comments labeled by crowdworkers.Each comment is labelled as either 'healthy' or 'unhealthy', in addition to binary labels for the presence of six potentially 'unhealthy' sub-attributes: (1) hostile; (2) antagonistic, insulting, provocative or trolling; (3) dismissive; (4) condescending or patronising; (5) sarcastic; and/or (6) an unfair generalisation.Each label also has an associated confidence score.We argue that there is a need for datasets which enable research based on a broad notion of 'unhealthy online conversation'.We build this typology to encompass a substantial proportion of the individual comments which contribute to unhealthy online conversation.For some of these attributes, this is the first publicly available dataset of this scale.We explore the quality of the dataset, present some summary statistics and initial models to illustrate the utility of this data, and highlight limitations and directions for further research.
MAF: Multimodal Alignment Framework for Weakly-Supervised Phrase Grounding

Phrase localization is a task that studies the mapping from textual phrases to regions of an image.Given difficulties in annotating phraseto-object datasets at scale, we develop a Multimodal Alignment Framework (MAF) to leverage more widely-available caption-image datasets, which can then be used as a form of weak supervision.We first present algorithms to model phrase-object relevance by leveraging fine-grained visual representations and visually-aware language representations.By adopting a contrastive objective, our method uses information in caption-image pairs to boost the performance in weakly-supervised scenarios.Experiments conducted on the widely-adopted Flickr30k dataset show a significant improvement over existing weaklysupervised methods.With the help of the visually-aware language representations, we can also improve the previous best unsupervised result by 5.56%.We conduct ablation studies to show that both our novel model and our weakly-supervised strategies significantly contribute to our strong results. 1
Afterpulse analysis for passive decoy quantum key distribution

The passive decoy method, a candidate for high-speed quantum key distribution (QKD), can prepare laser pulses with various photon-number distributions according to the responses of the transmitter's detectors, which usually adopts the single-photon avalanche photodiode (SPAD) in QKD. As an intrinsic characteristic of the SPAD, the afterpulse can introduce random detector responses, however, is regrettably ignored in passive schemes. Here we develop an analytical model for passive schemes in the finite-resource regime to describe the afterpulse effect on the detectors of both transmitter and receiver. The photon-number distributions biased by afterpulses are corrected, which reveals that the secure key rate is significantly overrated when the afterpulse is ignored. Moreover, our model can increase the tolerance of passive decoy source to afterpulse effect as the result shows that the secret key rates are comparable at different afterpulse probability by reoptimization.
A Taxonomy for Deep Learning in Natural Language Processing

Despite a large number of available techniques around Deep Learning in Natural Language Processing (NLP), no holistic framework exists which supports researchers and practitioners to organise knowledge when designing, comparing and evaluating NLP applications.This paper addresses this lack of a holistic framework by developing a taxonomy for Deep Learning in Natural Language Processing.Based on a systematic literature review as proposed by Webster and Watson [1] and vom Brocke et al. [2] and the iterative taxonomy development process of Nickerson et al. [3] we derived five novel dimensions and 38 characteristics based on a sample of 205 papers.Our research suggests, that a Deep Learning NLP approach can be distinguished by five dimensions which were partly derived from the CRISP-DM methodology: application understanding, data preparation, modeling, learning technique and evaluation.We, therefore, hope to provide guidance and support for researchers and practitioners when using Deep Learning for NLP to design, compare and evaluate NLP applications.
Entrepreneurial training effectiveness, government entrepreneurial supports and venturing of TVET students into IT related entrepreneurship – An indirect-path effects analysis

Over the periods of 2015–2018 there have been declining trends for Nigeria in Global Entrepreneurship Index (GEI) signifying a major challenge to its entrepreneurship development. To understand some of the explanatory variables for this phenomenon Theory of Planned Behavior (TPB) was deployed and extended to examine indirect effects of entrepreneurial training effectiveness and government entrepreneurial supports on the Technical and Vocational Education and Training (TVET) students venturing intention into IT related entrepreneurship through attitude and perceived behavioral control for gaining insights to address the aforesaid challenge. Quantitative approach was employed through data collected from sampled TVET students which was analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM). The result supports all the hypothesized indirect-path effects. It implied that naturally built attitude and behavioral control alone cannot influence TVET students venturing intention; it needs enhancement through effective entrepreneurial training and government entrepreneurial supports. The research opens new research paradigm for studying the antecedents of the original determinants of intention through indirect path analyses. Applying these findings can reduce the reliance on public sector for employment. It could equally create jobs that could reduce societal crisis through employment into privately owned businesses.
Online Event Detection in Synchrophasor Data with Graph Signal Processing

Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency.
Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection

Recent empirical works show that large deep neural networks are often highly redundant and one can find much smaller subnetworks without a significant drop of accuracy. However, most existing methods of network pruning are empirical and heuristic, leaving it open whether good subnetworks provably exist, how to find them efficiently, and if network pruning can be provably better than direct training using gradient descent. We answer these problems positively by proposing a simple greedy selection approach for finding good subnetworks, which starts from an empty network and greedily adds important neurons from the large network. This differs from the existing methods based on backward elimination, which remove redundant neurons from the large network. Theoretically, applying the greedy selection strategy on sufficiently large {pre-trained} networks guarantees to find small subnetworks with lower loss than networks directly trained with gradient descent. Our results also apply to pruning randomly weighted networks. Practically, we improve prior arts of network pruning on learning compact neural architectures on ImageNet, including ResNet, MobilenetV2/V3, and ProxylessNet. Our theory and empirical results on MobileNet suggest that we should fine-tune the pruned subnetworks to leverage the information from the large model, instead of re-training from new random initialization as suggested in \citet{liu2018rethinking}.
Development of Methods for the Strategic Management of Web Projects

In this article, we have explored methods for the strategic management of web projects. By introducing a long-term web project development strategy into the operation of a web project, mechanisms can be developed to improve the efficiency and effectiveness of the web project. An important factor is to develop a strategy taking into account all possible crisis situations and ways out of these situations. The authors analyzed and simulated the web project structure, working out methods of web project strategy realization and implementation in a crisis situation. Additionally, in this article, the authors have presented the model for the strategic map of the balanced scorecard of a web project. The authors tested the developed methods on six web projects of university departments. The received results confirmed the appropriateness and necessity of the development and implementation of methods of the strategic management of web projects.
Research on Logistics Information Blockchain Data Query Algorithm Based on Searchable Encryption

In order to ensure the security of logistics information and to query information quickly and efficiently, using searchable encryption algorithms, combined with the characteristics of the blockchain, a searchable and encrypted logistics information blockchain data query algorithm is proposed.First, the logistics information is divided into multiple data files, encrypted with an asymmetric searchable encryption algorithm, and then stored in the cloud server.The keyword index value is extracted from each data file and uploaded to the blockchain.This solution can be used at any time Update and query data.Finally, analyze the correctness, completeness and safety of the scheme of this article, which proves the feasibility of this scheme.
Generative adversarial network based data augmentation to improve cervical cell classification model

<abstract> The survival rate of cervical cancer can be improved by the early screening. However, the screening is a heavy task for pathologists. Thus, automatic cervical cell classification model is proposed to assist pathologists in screening. In cervical cell classification, the number of abnormal cells is small, meanwhile, the ratio between the number of abnormal cells and the number of normal cells is small too. In order to deal with the small sample and class imbalance problem, a generative adversarial network (GAN) trained by images of abnormal cells is proposed to obtain the generated images of abnormal cells. Using both generated images and real images, a convolutional neural network (CNN) is trained. We design four experiments, including 1) training the CNN by under-sampled images of normal cells and the real images of abnormal cells, 2) pre-training the CNN by other dataset and fine-tuning it by real images of cells, 3) training the CNN by generated images of abnormal cells and the real images, 4) pre-training the CNN by generated images of abnormal cells and fine-tuning it by real images of cells. Comparing these experimental results, we find that 1) GAN generated images of abnormal cells can effectively solve the problem of small sample and class imbalance in cervical cell classification; 2) CNN model pre-trained by generated images and fine-tuned by real images achieves the best performance whose AUC value is 0.984. </abstract>
Blockchain and Higher Education Diplomas

Due to added mobility and the increase in international students worldwide, as well as the current problem regarding the counterfeiting of diplomas and the selling of fraudulent certificates, we propose a technological solution. Namely, to ally blockchain technology to higher education certificates and diplomas, to make the process of checking for academic qualifications more facilitated and transparent. Employers of graduates, as well as higher education institutions which evaluate course applicants, would benefit. Perhaps equally as important, students applying for international degree programs would have their lives simplified. There is an increased pressure to ensure the legitimacy and authenticity of certifications and diplomas—and preferably without the current “hassle” of getting diplomas recognized by official entities. New technological advances, with the development of blockchain and smart contracts, with their characteristics of immutability, decentralization, security, traceability, and consensus, may be considered an excellent match to implement a robust and reliable anti-fraud solution to issue digital diplomas. Radical innovations, such as linking blockchain and higher education diplomas, involve significant change and novelty. Linking blockchain and higher education diplomas could potentially positively impact and benefit millions of people worldwide, especially the younger generations. This study involved a literature review and the searching of the Scopus database (refereed publications) for the following concepts: blockchain and diploma. Existing literature is recent, with most articles (25) published between 2019 and 2020, with 4 in 2018 and only 1 in 2017. This was aligned with our expectations since the development of blockchain utilization outside financial and crypto-assets industries is recent, and it is known as “Blockchain 3.0”. We can additionally affirm that the topic is attracting attention and efforts from researchers worldwide and that some higher education institutions have already implemented ad hoc solutions. As it is, the sector lacks a unified response to the problem of automatic and reliable higher education diploma certification.
ZoomInNet: A Novel Small Object Detector in Drone Images with Cross-Scale Knowledge Distillation

Drone-based object detection has been widely applied in ground object surveillance, urban patrol, and some other fields. However, the dramatic scale changes and complex backgrounds of drone images usually result in weak feature representation of small objects, which makes it challenging to achieve high-precision object detection. Aiming to improve small objects detection, this paper proposes a novel cross-scale knowledge distillation (CSKD) method, which enhances the features of small objects in a manner similar to image enlargement, so it is termed as ZoomInNet. First, based on an efficient feature pyramid network structure, the teacher and student network are trained with images in different scales to introduce the cross-scale feature. Then, the proposed layer adaption (LA) and feature level alignment (FA) mechanisms are applied to align the feature size of the two models. After that, the adaptive key distillation point (AKDP) algorithm is used to get the crucial positions in feature maps that need knowledge distillation. Finally, the position-aware L2 loss is used to measure the difference between feature maps from cross-scale models, realizing the cross-scale information compression in a single model. Experiments on the challenging Visdrone2018 dataset show that the proposed method draws on the advantages of the image pyramid methods, while avoids the large calculation of them and significantly improves the detection accuracy of small objects. Simultaneously, the comparison with mainstream methods proves that our method has the best performance in small object detection.
Enhancing Human-Centered Design With Youth-Led Participatory Action Research Approaches for Adolescent Sexual and Reproductive Health Programming

Existing participatory research approaches have failed to identify innovative methods that overcome the persistent barriers to adolescent sexual and reproductive health service demand and access. Increasingly, programmers have turned to human-centered design (HCD), a problem-solving process that centers the needs, perspectives, and experiences of people, when developing solutions to complex SRH challenges. This article describes the application of a youth-engaged version of HCD as part of Adolescents 360, a transdisciplinary initiative to increase 15- to 19-year-old girls’ use of modern contraception in Nigeria, Ethiopia, and Tanzania. Youth-adult design teams (including 111 “youth designers” trained in HCD methods) undertook formative research to inform the design and implementation of interventions. We reflect on the practical implications of using instrumental strategies of HCD with a youth-led participatory approach. Our experience indicates that (1) engaging youth as project partners in transdisciplinary teams requires planned and dedicated financial and human resources; (2) involving youth as action researchers can help identify opportunities to improve program empathy and responsiveness; (3) it is difficult to recruit “extreme users” as project partners because of the high competencies needed in HCD; (4) centering empathy and employing design standards during prototyping can drive decision-making and resolve questions raised by conflicting evidence claims in existing bodies of literature; and (5) testing tangible services and products in real-world settings continues long after the intervention design phase. Youth-adult partnership should continue throughout this iterative and adaptive phase to ensure that the adolescent experience of the intervention remains at the core of intervention delivery.
Multi-Image Encryption Based on Compressed Sensing and Deep Learning in Optical Gyrator Domain

In this paper, a multi-image encryption scheme based on compressed sensing (CS) and deep learning in the optical gyrator domain is proposed.Firstly, multiple plaintext images are compressed by CS to obtain multiple measurements, and then the pixels of each measurement are scrambled by using a chaotic system.Secondly, the scrambled measurements are combined into a matrix and diffused by XOR operation with a chaotic matrix.Finally, the diffused matrix is encoded with a random phase and an optical gyrator transform to obtain a complex-valued matrix, and the amplitude of the complex-valued matrix is taken as the ciphertext.In decrypt, plaintext images are reconstructed from the CS measurements by a neural network, which achieves high reconstruction speed and quality compared with the traditional algorithm.Especially, the data amount of plaintext images can be compressed by up to 8 times while achieving high decryption quality.To our best knowledge, CS reconstruction algorithms based on deep learning is firstly used for image encryption.Moreover, the proposed scheme is highly robust against occlusion, noise, and chosen-plaintext attack.
Educational Inequality and COVID‑19 Pandemic: Relationship between the Family Socio-Economic Status and Student Experience of Remote Learning

The urgent transition to distance education in March 2020 revitalized the debate on the influence of a new educational reality on the educational inequality. A study was performed to measure the relationship between the difficulties experienced by students during distance learning and their socioeconomic status. Data from a nationwide survey of students administered in late March — early April 2020 by a team of researchers from a few Russian universities was used as empirical basis of this study. Results demonstrate significant differences in obstacles faced by students from families with different levels of income. Students from low-income families were the most likely to have technical and self-regulation problems and to lack skills required for effective distance learning. Findings indicate the importance of finding system-level solutions to ensure equal opportunities for students in distance learning, regardless of their socioeconomic status.
Speculation and the Design of Development

This paper examines the role of technoscientific speculation in large-scale development projects in postcolonial spaces, building on recent work in STS, design research, and postcolonial studies in and beyond CSCW. We analyze two historical cases of technology-infused development projects in the Canadian province of Newfoundland and Labrador and in Jamaica. We find that speculation in these contexts remixes the constructive stance toward speculation typical for normative technoscience with the critical, contesting orientation of speculative design. Conflicts between these stances are resolved by leveraging fantasy for pragmatic ends, grounding audacious fictions in imported realities, unmooring from conventional understandings of linear technological progress, and using even conservative futures to trouble colonial conventions.
DECA: A Dynamic Energy Cost and Carbon Emission-Efficient Application Placement Method for Edge Clouds

As an increasing amount of data processing is done at the network edge, high energy costs and carbon emission of Edge Clouds (ECs) are becoming significant challenges. The placement of application components (e.g., in the form of containerized microservices) on ECs has an important effect on the energy consumption of ECs, impacting both energy costs and carbon emissions. Due to the geographic distribution of ECs, there is a variety of resources, energy prices and carbon emission rates to consider, which makes optimizing the placement of applications for cost and carbon efficiency even more challenging than in centralized clouds. This paper presents a Dynamic Energy cost and Carbon emission-efficient Application placement method (DECA) for ECs. DECA addresses both the initial placement of applications on ECs and the re-optimization of the placement using migrations. DECA considers geographically varying energy prices and carbon emission rates as well as optimizing the usage of both network and computing resources at the same time. By combining a prediction-based A* algorithm with a Fuzzy Sets technique, DECA makes intelligent decisions to optimize energy cost and carbon emissions. Simulation results show the ability of DECA in providing a tradeoff and optimizing energy cost and carbon emission at the same time.
Consistent Meta-Regularization for Better Meta-Knowledge in Few-Shot Learning

Recently, meta-learning provides a powerful paradigm to deal with the few-shot learning problem. However, existing meta-learning approaches ignore the prior fact that good meta-knowledge should alleviate the data inconsistency between training and test data, caused by the extremely limited data, in each few-shot learning task. Moreover, legitimately utilizing the prior understanding of meta-knowledge can lead us to design an efficient method to improve the meta-learning model. Under this circumstance, we consider the data inconsistency from the distribution perspective, making it convenient to bring in the prior fact, and propose a new consistent meta-regularization (Con-MetaReg) to help the meta-learning model learn how to reduce the data-distribution discrepancy between the training and test data. In this way, the ability of meta-knowledge on keeping the training and test data consistent is enhanced, and the performance of the meta-learning model can be further improved. The extensive analyses and experiments demonstrate that our method can indeed improve the performances of different meta-learning models in few-shot regression, classification, and fine-grained classification.