Reviewerly/exHarmony huggingface dataset:
Cyber Security in the Age of COVID-19

As a containment strategy for the dreaded Corona Virus Disease 19 (COVID 19) which is spreading rapidly and causing severe damage to life and economy of nations, places of public gathering like schools, places of religious worship, open physical markets, offices as well as venues for social meetings (such as clubs) are been closed down, to promote social distancing in most nations across the globe. Therefore, most public/private organizations, and even individuals have resorted to the use of diverse Information Technologies (IT) for connecting themselves and other life essentials. Educational, agricultural, religious and even health institutions now deliver their services to users/clients and receive payments via online platforms, students study from home, even employees of most organizations now work remotely (maybe from their homes). Moreover, there is a sharp growth in demand for food deliveries and online grocery. The massive adoption of IT by almost all aspects of human life especially during this epidemic has also led to increased cyber security concerns. Cybercriminals and other individuals with malicious intent now take COVID-19 as an opportunity to perpetrate cybercrimes, especially for monetary gains. Domestic violence seems to be on the rise perhaps due to the lockdown, contact tracing approaches are massively been developed and used, healthcare systems are being attacked with ransom ware and resources such as patient records confidentiality, and integrity are being compromised. Individuals are falling victim to phishing attacks through COVID-19 related content. This paper presents an extensive study of major cyber security concerns that are and could take place during the COVID 19 pandemic as well as strategies for mitigating them.
Maximal relevance and optimal learning machines

Abstract We explore the hypothesis that learning machines extract representations of maximal relevance, where the relevance is defined as the entropy of the energy distribution of the internal representation. We show that the mutual information between the internal representation of a learning machine and the features that it extracts from the data is bounded from below by the relevance. This motivates our study of models with maximal relevance—that we call optimal learning machines—as candidates of maximally informative representations. We analyse how the maximisation of the relevance is constrained both by the architecture of the model used and by the available data, in practical cases. We find that sub-extensive features that do not affect the thermodynamics of the model, may affect significantly learning performance, and that criticality enhances learning performance, but the existence of a critical point is not a necessary condition. On specific learning tasks, we find that (i) the maximal values of the likelihood are achieved by models with maximal relevance, (ii) internal representations approach the maximal relevance that can be achieved in a finite dataset and (iii) learning is associated with a broadening of the spectrum of energy levels of the internal representation, in agreement with the maximum relevance hypothesis.
Meta-DETR: Few-Shot Object Detection via Unified Image-Level Meta-Learning

Few-shot object detection aims at detecting novel objects with only a few annotated examples. Prior works have proved meta-learning a promising solution, and most of them essentially address detection by meta-learning over regions for their classification and location fine-tuning. However, these methods substantially rely on initially well-located region proposals, which are usually hard to obtain under the few-shot settings. This paper presents a novel meta-detector framework, namely Meta-DETR, which eliminates region-wise prediction and instead meta-learns object localization and classification at image level in a unified and complementary manner. Specifically, it first encodes both support and query images into category-specific features and then feeds them into a category-agnostic decoder to directly generate predictions for specific categories. To facilitate meta-learning with deep networks, we design a simple but effective Semantic Alignment Mechanism (SAM), which aligns high-level and low-level feature semantics to improve the generalization of meta-learned representations. Experiments over multiple few-shot object detection benchmarks show that Meta-DETR outperforms state-of-the-art methods by large margins.
An Efficient Quantum Key Distribution Protocol with Dense Coding on Single Photons

Combined with the dense coding mechanism and the bias-BB84 protocol, an efficient quantum key distribution protocol with dense coding on single photons (QDKD-SP) is proposed. Compared with the BB84 or bias-BB84 protocols base... | Find, read and cite all the research you need on Tech Science Press
Pedagogical use of mobile technologies during Coronavirus school closure

The unplanned closure of schools due to Coronavirus Disease 2019 (COVID-19) left millions of students out of schools. However, the use of mobile technologies offered opportunities for educators and students to engage in remote pedagogical activities during the school closures. The study investigated the effects of the usage of mobile technologies on educators’ and students’ pedagogical activities during the COVID-19 school closure, and on the students’ attitudes towards pedagogical activities during the COVID-19 school closure. A total of 150 questionnaires were administered to participants that consist of educators and students from different levels of education in Nigeria. The samples were selected through simple random sampling technique, and collected data were analyzed using percentages, frequencies, and statistical methods of regression and ANOVA. The results show that the use of mobile technologies had statistically significant effects on the pedagogical activities of students and educators during the COVID-19 school closure, and on the attitudes of students towards pedagogical activities during the COID-19 school closure. Majority of the participants agreed that the use of mobile technologies such as: laptops, mobile phones, mobile apps, MP3, and other PDAs positively influenced their pedagogical activities during the COVID-19 school closure. The study concludes that mobile technologies have become an important and promising pedagogical tool which can be harnessed to enhance ubiquitousness and continued education during and after school closure or outbreak of pandemics.
Classifying Microscopic Acute and Old Myocardial Infarction Using Convolutional Neural Networks

Abstract Convolutional neural network (CNN) has advanced in recent years and translated from research into medical practice, most notably in clinical radiology and histopathology. Research on CNNs in forensic/postmortem pathology is almost exclusive to postmortem computed tomography despite the wealth of research into CNNs in surgical/anatomical histopathology. This study was carried out to investigate whether CNNs are able to identify and age myocardial infarction (a common example of forensic/postmortem histopathology) from histology slides. As a proof of concept, this study compared 4 CNNs commonly used in surgical/anatomical histopathology to identify normal myocardium from myocardial infarction. A total of 150 images of the myocardium (50 images each for normal myocardium, acute myocardial infarction, and old myocardial infarction) were used to train and test each CNN. One of the CNNs used (InceptionResNet v2) was able to show a greater than 95% accuracy in classifying normal myocardium from acute and old myocardial infarction. The result of this study is promising and demonstrates that CNN technology has potential applications as a screening and computer-assisted diagnostics tool in forensic/postmortem histopathology.
Fuzzy Search for Multiple Chinese Keywords in Cloud Environment

With the continuous development of cloud computing and big data technology, the use of cloud storage is more and more extensive, and a large amount of data is outsourced for public cloud servers, and the security problems tha... | Find, read and cite all the research you need on Tech Science Press
An Influence Maximization Algorithm Based On The Mixed Importance Of Nodes

The influence maximization is the problem of finding k seed nodes that maximize the scope of influence in a social network. Therefore, the comprehensive influence of node needs to be considered, when we choose the most influe... | Find, read and cite all the research you need on Tech Science Press
BlendTorch: A Real-Time, Adaptive Domain Randomization Library

Solving complex computer vision tasks by deep learning techniques rely on large amounts of (supervised) image data, typically unavailable in industrial environments. Consequently, the lack of training data is beginning to impede the successful transfer of state-of-the-art computer vision methods to industrial applications. We introduce BlendTorch, an adaptive Domain Randomization (DR) library, to help create infinite streams of synthetic training data. BlendTorch generates data by massively randomizing low-fidelity simulations and takes care of distributing artificial training data for model learning in real-time. We show that models trained with BlendTorch repeatedly perform better in an industrial object detection task than those trained on real or photo-realistic datasets.
Superconducting circuit architecture for digital-analog quantum computing

Abstract We propose a superconducting circuit architecture suitable for digital-analog quantum computing (DAQC) based on an enhanced NISQ family of nearest-neighbor interactions. DAQC makes a smart use of digital steps (single qubit rotations) and analog blocks (parametrized multiqubit operations) to outperform digital quantum computing algorithms. Our design comprises a chain of superconducting charge qubits coupled by superconducting quantum interference devices (SQUIDs). Using magnetic flux control, we can activate/deactivate exchange interactions, double excitation/de-excitations, and others. As a paradigmatic example, we present an efficient simulation of an $\ell \times h$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ℓ</mml:mi> <mml:mo>×</mml:mo> <mml:mi>h</mml:mi> </mml:math> fermion lattice (with $2&lt;\ell \leq h$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mn>2</mml:mn> <mml:mo>&lt;</mml:mo> <mml:mi>ℓ</mml:mi> <mml:mo>≤</mml:mo> <mml:mi>h</mml:mi> </mml:math> ), using only $2(2\ell +1)^{2}+24$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mn>2</mml:mn> <mml:msup> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>2</mml:mn> <mml:mi>ℓ</mml:mi> <mml:mo>+</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>+</mml:mo> <mml:mn>24</mml:mn> </mml:math> analog blocks. The proposed architecture design is feasible in current experimental setups for quantum computing with superconducting circuits, opening the door to useful quantum advantage with fewer resources.
An Optimization of Language Learning in Writing Through E-Learning: Encountering Covid-19 Pandemic

In response to the current situation of the pandemic covid-19 outbreak which has been affected worldwide, this study examines the opt on online learning for writing skill, and to assess the development of the writing task. It draws the data from an English as a Foreign Language (EFL) studentâ€™s writing documents, and marking documents of three experienced English teachersâ€™s using the â€œAnalytical Marking Schemeâ€. A qualitative approach was employed where the data was obtained during the online supervision of the studentâ€™s final project (TA) by integrating visual images into her writing project. The participant is in her final year at English Department, Politeknik Negeri Padang who focuses on writing brochure as her TA. The findings of this study show that there are two major progresses found in the writing documents of the first draft to the final draft. First, the content of the writing has been improved as seen from the variation of vocabulary used within. The essay has been organized well where the ideas are developed, and the information shared is logical and complete. Second, even though there is still a few errors occured in grammar, punctuation, and spelling, they have no influence on the idea conveyed. The student is able to adjust into the online learning, and increase her writing performance by utilizing the visual images given. The findings suggest that there should be more innovative learning designs to accommodate the needs of online learning. Since this type of learning is challenging for both teachers and student, teachers need to be able to put their role as facilitator to establish more engagements with the student, and to trigger the student to be more autonomous in learning.Â
Remote management of library staff: Challenges and practical solutions

COVID-19 has forced staff in academic libraries across the world to pivot from face-to-face workdays and services to fully remote (and, in some cases, back again) with very little time or notice. This new reality has presented new challenges in the remote management of staff that may also be working remotely, or in the building. This column explores some of those challenges and presents possible solutions for those at all levels of library management.
A Framework Of Big Data As Service Platform For Access Control &amp; Privacy Protection Using Blockchain Network

Big Data As A Service Is Used In Today’s Scenario To Handle And Process The Big Amount Of Data Which Are Generated From Different Source Every Day. Since Data Is Stored On The Cloud Platform, The System Could Suffer A Failure And Give Attackers The Opportunity To Launch Various Categories Of Attacks.Manyresearcheshave Been Done In This Domain To Provide Security And Protection To The Data On Cloud. The Blockchain Technology Is A Secure, Distributed And Privacy-Preserving Decentralized Ledger Where The Transactions Are Flexible, Secure,Verifiable And Permanent Way.Here, The Transaction Data Is Encrypted Andkept In A Wrapped Block (I.E., Record) Which Are Spreadthrough The N/W In A Provable And Unabashedmode Across The Entire Network To Enhance Information Security And Data Privacy. In This Paperwe Have Proposed A Framework For An Access Control With Privacy Protection In Bdaas Based On Blockchain Technology. Here Blockchain Technology Is Used Only For Storing The Transaction Log Information Whenever Any Kind Of Event Log Occurred In System.
Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog

Arun Babu, Akshat Shrivastava, Armen Aghajanyan, Ahmed Aly, Angela Fan, Marjan Ghazvininejad. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.
Disentangling copy-moved source and target areas

Copy-move is a very popular image falsification where a semantically coherent part of the image, the source area, is copied and pasted at another position within the same image as the so-called target area. The majority of existing copy-move detectors search for matching areas and thus identify the source and target zones indifferently, while only the target really represents a tampered area. To the best of our knowledge, at the moment of preparing this paper there has been only one published method called BusterNet that is capable of performing source and target disambiguation by using a specifically designed deep neural network. Different from the deep-learning-based BusterNet method, we propose in this paper a source and target disentangling approach based on local statistical model of image patches. Our proposed method acts as a second-stage detector after a first stage of copy-move detection of duplicated areas. We had the following intuition: even if no manipulation (e.g., scaling and rotation) is added on target area, its boundaries should expose a statistical deviation from the pristine area and the source area; further, if the target area is manipulated, the deviation should appear not only on the boundaries but on the full zone. Our method relies on machine learning tool with Gaussian Mixture Model to describe likelihood of image patches. Likelihoods are then compared between the pristine region and the candidate source/target areas as identified by the first-stage detector. Experiments and comparisons demonstrate the effectiveness of the proposed method.
HR-Chat bot: Designing and Building Effective Interview Chat-bots for Fake CV Detection

The Human Resources (HR) departments in every company can face challenges regarding the number of job applicants that are applying for a job. So; The main idea of this paper is to help HR employees in collecting information about applicants to a specific job through identifying applicant's personal skills and through writing their CV in an organized manner and filtering or rearranging job applicants throw a chatbot interview that can discover fake CV skills and find applicant's personal skills. Afterward, the proposed model will recommend this applicant according to the personal skills the job requires. The model based on creating a chatBot to extract these skills through an interview. Also, the proposed model will provide facial expression recognition techniques to track the applicant's expressions throughout the interview. This virtual interviews could highly minimize the amount of effort the HR employee have to deal with. Also it rearranges job applicants to the right track.
Demonstrating the Reliability of Self-Annotated Emotion Data

Vent is a specialised iOS/Android social media platform with the stated goal to encourage people to post about their feelings and explicitly label them.In this paper, we study a snapshot of more than 100 million messages obtained from the developers of Vent, together with the labels assigned by the authors of the messages.We establish the quality of the self-annotated data by conducting a qualitative analysis, a vocabularybased analysis, and by training and testing an emotion classifier.We conclude that the selfannotated labels of our corpus are indeed indicative of the emotional contents expressed in the text and thus can support more detailed analyses of emotion expression on social media, such as emotion trajectories and factors influencing them.
A Hybrid Personalized Scientific Paper Recommendation Approach Integrating Public Contextual Metadata

Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest.Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles.Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly.However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users.Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions.Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module.Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy.In order to verify the effectiveness of the proposed approach, publicly available datasets were employed.Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.
UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data

In this paper, we propose a unified pre-training approach called UniSpeech to learn speech representations with both unlabeled and labeled data, in which supervised phonetic CTC learning and phonetically-aware contrastive self-supervised learning are conducted in a multi-task learning manner. The resultant representations can capture information more correlated with phonetic structures and improve the generalization across languages and domains. We evaluate the effectiveness of UniSpeech for cross-lingual representation learning on public CommonVoice corpus. The results show that UniSpeech outperforms self-supervised pretraining and supervised transfer learning for speech recognition by a maximum of 13.4% and 17.8% relative phone error rate reductions respectively (averaged over all testing languages). The transferability of UniSpeech is also demonstrated on a domain-shift speech recognition task, i.e., a relative word error rate reduction of 6% against the previous approach.
A Color Elastica Model for Vector-Valued Image Regularization

Models related to the Euler's elastica energy have proven to be useful for many applications including image processing. Extending elastica models to color images and multichannel data is a challenging task, as stable and consistent numerical solvers for these geometric models often involve high order derivatives. Like the single channel Euler's elastica model and the total variation models, geometric measures that involve high order derivatives could help when considering image formation models that minimize elastic properties. In the past, the Polyakov action from high energy physics has been successfully applied to color image processing. Here, we introduce an addition to the Polyakov action for color images that minimizes the color manifold curvature. The color image curvature is computed by applying the Laplace--Beltrami operator to the color image channels. When reduced to gray-scale images, while selecting appropriate scaling between space and color, the proposed model minimizes Euler's elastica operating on the image level sets. Finding a minimizer for the proposed nonlinear geometric model is a challenge we address in this paper. Specifically, we present an operator-splitting method to minimize the proposed functional. The nonlinearity is decoupled by introducing three vector-valued and matrix-valued variables. The problem is then converted into solving for the steady state of an associated initial-value problem. The initial-value problem is time split into three fractional steps, such that each subproblem has a closed form solution, or can be solved by fast algorithms. The efficiency and robustness of the proposed method are demonstrated by systematic numerical experiments.
Plot and Rework: Modeling Storylines for Visual Storytelling

Writing a coherent and engaging story is not easy.Creative writers use their knowledge and worldview to put disjointed elements together to form a coherent storyline, and work and rework iteratively toward perfection.Automated visual storytelling (VIST) models, however, make poor use of external knowledge and iterative generation when attempting to create stories.This paper introduces PR-VIST, a framework that represents the input image sequence as a story graph in which it finds the best path to form a storyline.PR-VIST then takes this path and learns to generate the final story via a re-evaluating training process.This framework produces stories that are superior in terms of diversity, coherence, and humanness, per both automatic and human evaluations.An ablation study shows that both plotting and reworking contribute to the model's superiority.