Reviewerly/exHarmony huggingface dataset:
Fan activism as discursive action: Poaching foreign television series for political satire in China

Critical discourse analysis (CDA) has proved insightful theoretically to analysing discourses and discursive practices of digital activism, especially political activism online. However, few studies have developed CDA analytical framework to unpack fan activism. The unique feature of fan activism lies in the discursive dynamics of transferring discursive resources from entertainment products to construct political discourse. Drawing on CDA, this research explicates the question of how global political TV fictions such as the Netflix TV series House of Cards (HoC) are appropriated as discursive resources for emergent online fan activism in local political contexts. Constrained by the authoritarian context, China's fan activism usually showcases as discursive action. Using the TV series HoC as a lens, Chinese HoC fans engage in online fan activism through the discursive practice of political satire, playfully criticising contemporary China's authoritarian politics. Conceptualised within CDA, this research attempts to make explicit a three-pronged analytical framework to unpack such discursive action. To use Western TV series to satirise Chinese contemporary politics, firstly, these fans map the fictional world of HoC onto China's real-life politics by adopting the lexical strategy of mixing specific Chinese political terms and those taken from televisual and real American politics. Secondly, analogies between the content-world of HoC and contemporary Chinese politics are used to criticise contemporary Chinese political figures and scandals. Thirdly, to avoid Internet censorship, various discursive strategies are used by HoC fans to express their criticism subtly. By focusing on fan activism's discursive dynamics, this study also forms a dialogue with the scholarship on China's online activism.
Multimodal Brain Tumor Classification

Cancer is a complex disease that provides various types of information depending on the scale of observation. While most tumor diagnostics are performed by observing histopathological slides, radiology images should yield additional knowledge towards the efficacy of cancer diagnostics. This work investigates a deep learning method combining whole slide images and magnetic resonance images to classify tumors. In particular, our solution comprises a powerful, generic and modular architecture for whole slide image classification. Experiments are prospectively conducted on the 2020 Computational Precision Medicine challenge, in a 3-classes unbalanced classification task. We report cross-validation (resp. validation) balanced-accuracy, kappa and f1 of 0.913, 0.897 and 0.951 (resp. 0.91, 0.90 and 0.94). For research purposes, including reproducibility and direct performance comparisons, our finale submitted models are usable off-the-shelf in a Docker image available at https://hub.docker.com/repository/docker/marvinler/cpm_2020_marvinler .
Romanized Sindhi Rules for Text Communication

Sindhi is one of the historical languages which is widely used in all over the world, but especially in the province of Sindh Pakistan. Sindhi language has its own script and written by the right-handed. Nowadays the use of different Sindhi platforms is increasing especially for communication. The majority of the people of Sindh province read, write and speak very well, but they face the problem in text communication while using different communication platforms. However, the users of computer and mobile phone feel trouble/difficulty during the use of the Sindhi script in typing of text messages, tweets and comments while using different platforms in computer and mobile phone. Natural Language Processing (NLP) is one of the better options for the solution of these problems of text communication on different platforms. For the proper solution of text communication issues, Romanized Sindhi text is used instead of Sindhi text. Romanized text writing is easier than the Sindhi text writing because Sindhi text writing needs the special type of keyboard while writing of Romanized text does not need any special type of keyboard. For the writing of Romanized Sindhi text, rules are defined in this paper which provide easiness during writing and understanding of the text. Romanized Sindhi Rules (RSR) are simple and easy to understand the meaning of the text and provide fast communication (text). This study is also helpful for further research in the Romanized Sindhi text by using different approaches and provides easiness in communication.
Scanning-to-speech challenge-response authentication test for visually impaired

Completely Automated Public Turing Tests to Tell Computer and Humans Apart (CAPTCHA) is an approach used to distinguish between a self-active computer program and a human. This kind of CAPTCHA is used in the field of computing to discover a particular user interacting with the system is human or not. It requires someone to enter the correct sequence of digits, characters, or both the combinations. The average time taken to solve the current captcha displayed will take around 10 s approximately. Research findings also discover additionally that the numerous famous CAPTCHA procedures are not powerful or secure, further entangling the test of offering services acquired from robotic intervention yet available to individuals with disabilities. This leads to the formulation of new approaches like QR Code Scanning-to-Speech, Revamping, and Randomness. This technique is encrypted end-to-end by using Blowfish Fermat Little Theorem (BFLT). There are a total of 156 physical users (both visually impaired and visually fit) were analyzed to assess the potency of the proposed approach. Further, it is compared with the current highest development CAPTCHA to test the performance of the QR Code Scanning-to-Speech, Revamping, and Randomness. The security scrutiny proved that the proposed approach is robust and hindering Hidden Markov Model (HMM), Fuzzy solver, and recent eminent attacks.
An Interactive NL2SQL Approach with Reuse Strategy

This paper studies a recently proposed task that maps contextual natural language questions to SQL queries in a multi-turn interaction. Instead of synthesizing an SQL query in an end-to-end way, we propose a new model which first generates an SQL grammar tree, called Tree-SQL, as the intermediate representation, and then infers an SQL query from the Tree-SQL with domain knowledge. For semantic dependency among context-dependent questions, we propose a reuse strategy that assigns a probability for each sub-tree of historical Tree-SQLs. On the challenging contextual Text-to-SQL benchmark SParC ( https://yale-lily.github.io/sparc ) with the 'value selection' task which includes values in queries, our approach achieves SOTA accuracy of 48.5% in question execution accuracy and 21.6% in interaction execution accuracy. In addition, we experimentally demonstrate the significant improvements on the reuse strategy.
A Novel Face Inpainting Approach Based on Guided Deep Learning

In the last few years, deep learning has shown significant improvement for many computer vision open problems, especially Image inpainting. Image inpainting is the process of filling missing regions across images. One of the most challenging problems in image inpainting is face inpainting. In this work, a new novel approach for face inpainting is proposed which can capture and preserve the identity of each human face in images while reproducing the missing irregular region in images. A two-stage cascaded model is proposed. It is composed of a shape-predictor of the key-points of the face followed by an inpainting network. The shape-predictor identifies the human face's structure-preserving its local points, i.e. eyes, mouth, nose, and then the inpainting network fills any random-irregular missing regions guided by the obtained knowledge as a priori. The effectiveness of the proposed model was evaluated using the CelebA dataset. The obtained results from the trained model outperform the recently proposed technique with contextual attention.
An Internet-based treatment for Flying Phobia using 360° images: Study protocol for a feasibility pilot study

Flying Phobia (FP) is a prevalent disorder that can cause serious interference in a person's life. ICBT interventions have already shown their efficacy in several studies, but studies in the field of specific phobias are still scarce. Moreover, few studies have investigated the feasibility of using different types of images in exposure scenarios in ICBTs and no studies have been carried out on the role of sense of presence and reality judgement. The aim of the present study is to explore the feasibility of an ICBT for FP (NO-FEAR Airlines) using two types of images with different levels of immersion (still and navigable images). A secondary aim is to explore the potential effectiveness of the two experimental conditions using two types of images compared to a waiting list control group. Finally, the role of navigable images compared to the still images in the level of anxiety, sense of presence, and reality judgement will also be explored. This paper presents the study protocol.This study is a three-armed feasibility pilot study with the following conditions: NO-FEAR Airlines with navigable images, NO-FEAR Airlines with still images, and a waiting list group. A minimum of 60 participants will be recruited. The intervention will have a maximum duration of 6 weeks. Measurements will be taken at four different moments: baseline, post-intervention, and two follow-ups (3- and 12-month). Participants' opinions, preference, satisfaction and acceptance regarding the images used in the exposure scenarios will be assessed. FP symptomatology outcomes will also be considered for secondary analyses. The anxiety, sense of presence, and reality judgement in the exposure scenarios will also be analysed.This study will conduct a pilot study on the feasibility of an ICBT for FP and it is the first one to explore the evaluation of patients of the two type of images (still and navigable) and the role of presence and reality judgement in exposure scenarios delivered through the Internet. Research in this field can have an impact on the way these scenarios are designed and developed, as well as helping to explore whether they have any effect on adherence.NCT03900559. Trial Registration date 3 April 2019, retrospectively registered.
Object Detection in Surveillance Using Deep Learning Methods: A Comparative Analysis

Unmanned aerial vehicle (UAV) technology has revolutionized the field globally in today's scenario. The UAV technologies enabled the activities to be efficiently monitored, identified, and analyzed. The principal constraints of the present surveillance system, along with closed-circuit television (CCTV) cameras, are limited surveillance coverage area and high latency in object detection. Deep learning embedded with UAVs has found to be effective in the tracking and monitoring of objects, thus overcoming the constraints mentioned above. Dynamic surveillance systems in the current scenario seek high-speed streaming, and object detection in real-time visual data has become a challenge over a reasonable time delay. The paper draws a comprehensive analysis of object detection deep learning architectures by classifying the research based on architecture, techniques, applications, and datasets. It has been found that RetinaNet is highly accurate while YOLOv3 is fast.
Exploring Metrics for the Analysis of Code Submissions in an Introductory Data Science Course

While data science education has gained increased recognition in both academic institutions and industry, there has been a lack of research on automated coding assessment for novice students. Our work presents a first step in this direction, by leveraging the coding metrics from traditional software engineering (Halstead Volume and Cyclomatic Complexity) in combination with those that reflect a data science project's learning objectives (number of library calls and number of common library calls with the solution code). Through these metrics, we examined the code submissions of 97 students across two semesters of an introductory data science course. Our results indicated that the metrics can identify cases where students had overly complicated codes and would benefit from scaffolding feedback. The number of library calls, in particular, was also a significant predictor of changes in submission score and submission runtime, which highlights the distinctive nature of data science programming. We conclude with suggestions for extending our analyses towards more actionable intervention strategies, for example by tracking the fine-grained submission grading outputs throughout a student's submission history, to better model and support them in their data science learning process.
Web3 and the Creative Industries: How Blockchains Are Reshaping Business Models

Web3, underpinned by blockchain technology, is an evolution of digital infrastructure, whereby protocol-enforced consensus mechanisms facilitate the direct (that is, peer-to-peer) exchange of value between users, removing the need for trusted intermediaries. Existing blockchain experiments seek to create artist-centric business models, dismantling agency-centred business models that brokered and organised connections between artists and their fans or buyers. By enabling the automation of payments, licensing, intellectual property management, contracting and governance, digital content storage and access, blockchain technology also enables artists to set the terms of their market participation. An emerging decentralised ‘internet of value’ has the potential to reshape creative industries business models. The extent to which creative workers and cultural products are transformed by these infrastructures may depend on the adaptive responsiveness of existing institutions (including law, philanthropy and finance) to technological opportunities, although it is also possible that change may occur regardless of institutional efforts. A research agenda for Web3 creative industries would therefore extend upon existing knowledge of creative work, policy and funding models, as well as the consequences of technological change for distribution and access. In this chapter, we discuss case studies from music (Ujo-music and dotblockchain), collectibles (cryptokitties), visual arts (dada.nyc), and storytelling (Cellarius) and the policy and research questions that arise. In addition to offering new methods for the buying and selling of creative works, with implications for how creative production is rewarded, some of these experiments are seeking to destabilise the digital economy more broadly and to create avenues for values-based markets that provide creators with alternatives to commercially-oriented income streams.
Handwritten recognition of Hiragana and Katakana characters based on template matching algorithm

Abstract Japanese has become one of the most popular foreign languages in Indonesia. From a survey conducted by The Japan Foundation in 2017 shows that Indonesia is the first largest student in Southeast Asia, therefore those interested in learning Japanese are quite popular in Indonesia. One of the things learned in Japanese is about writing Hiragana and Katakana characters, each of which has 46 standard characters. The research aims to implement a template matching algorithm for handwritten recognition of Hiragana and Katakana characters, which can later be implemented in application design for learning Hiragana and Katakana character writing. Through the application of template matching algorithm, the accuracy level of handwritten recognitions pattern matching reaches 89.8%, so the algorithm will be very suitable if implemented for Hiragana and Katakana characters writing learning applications.
A Tutorial Introduction to Monte Carlo Tree Search

This tutorial provides an introduction to Monte Carlo tree search (MCTS), which is a general approach to solving sequential decision-making problems under uncertainty using stochastic (Monte Carlo) simulation. MCTS is most famous for its role in Google DeepMind's AlphaZero, the recent successor to AlphaGo, which defeated the (human) world Go champion Lee Sedol in 2016 and world #1 Go player Ke Jie in 2017. Starting from scratch without using any domain-specific knowledge (other than the rules of the game), AlphaZero was able to defeat not only its predecessors in Go but also the best AI computer programs in chess (Stockfish) and shogi (Elmo), using just 24 hours of training based on MCTS and reinforcement learning. We demonstrate the basic mechanics of MCTS via decision trees and the game of tic-tac-toe.
Construction of Electric Power Meta Knowledge Graph Based on Electric Power Industry Terminology

Meta Knowledge Graph is mainly a knowledge graph formed by the most basic concept entity and its synonymous relationship, which is the most direct entry to show the basic knowledge of the industry. However, due to the complexity of its data sources and the lack of data description specification, it has always been a research difficulty. Based on the analysis of big data characteristics of the power industry and SG-CIM data characteristics of the power company, this paper takes power basic terms as the entrance, crawls complete term entries from the network, obtains entities and synonyms by parsing content, and forms association relationship, and then uses graph database and programming tools to solve the technical problems of Meta Knowledge Graph storage and user interaction. It provides a practical method and technical scheme for the construction of electric power meta knowledge graph.
The coherent information on the manifold of positive definite density matrices

This paper will explore the restriction of the coherent information to the positive definite density matrices in the special case where the quantum channels are strictly positive linear maps. The space of positive definite density matrices is equipped with an embedded submanifold structure of the real vector space of Hermitian matrices. These ensure that the n-shot coherent information is differentiable and allows for the computation of its gradient and Hessian. We show that any tensor products of critical points preserve being a critical point of the coherent information. Furthermore, we show that for any positive integer n, the maximally mixed state is always a critical point for the class of mixed unitary quantum channels with orthogonal, unitary Kraus operators. We determine when the maximally mixed state is a local maximum/minimum or saddle point, including its eigenvectors, for the class of Pauli-erasure channels when n is equal to 1. This class includes the dephrasure channel and Pauli channel and refines potential regions where super-additivity is thought to occur. These techniques can be used to study other optimization problems over density matrices and allow the use of manifold optimization algorithms and a better understanding of the quantum capacity problem by utilizing the first and second order geometry.
Performance analysis of SMRT-based color image watermarking in different color spaces

Watermarking is a generic strategy for overcoming numerous issues associated with multimedia security and digital rights management. The color image digital watermarking has not been given considerable attention, and there exists many applications that require them for copyright protection. An image-watermarking scheme based on Sequency based MRT (SMRT) for color image is proposed in this paper to achieve this goal. Choosing a color space for watermarking has always been a big question, as there are a number of color spaces. Primary objective is to find a better color space under the same method from among the frequently used color spaces. In the embedding phase, grayscale image watermark is embedded in the SMRT of R component of the cover image. Scaling factor is varied from 0.01 to 0.5 for evaluating performance of the methodology using PSNR, MSE, SSIM, IEM and NC. Performance of color image watermarking technique using SMRT in nine color spaces is analyzed. It is observed that embedding watermark in Cb channel of YCbCr color space is more imperceptible and robust compared to other color spaces considered. Simulation results show significant improvement in terms of imperceptibility. The robustness is high against GN attack as compared to other attacks.
Semantically enhanced cyber security model for industry 4.0: methodological framework

This paper deals with questions of cybersecurity in the Industry 4.0, existing cybersecurity models and their enhancement. Providing with a methodology for semantically enhancing and improving cybersecurity models, the paper promotes novel, adapted methodological frameworks for cybersecurity, by promoting diversity in characteristics, and quantifying both the assessments of existing level of cybersecurity, and potential impacts of change within the field.
Evolving Neural Style Transfer Blends

Neural style transfer is an image filtering technique used in both digital art practice and commercial software. We investigate blending the styles afforded by neural models via interpolation and overlaying different stylisations. In order to produce preset stylisation filters for the development of a casual creator app, we experiment with various MAP-Elites quality/diversity approaches to evolving style transfer blends with particular properties, while maintaining diversity in the population.
Distilled Replay: Overcoming Forgetting Through Synthetic Samples

Replay strategies are Continual Learning techniques which mitigate catastrophic forgetting by keeping a buffer of patterns from previous experiences, which are interleaved with new data during training. The amount of patterns stored in the buffer is a critical parameter which largely influences the final performance and the memory footprint of the approach. This work introduces Distilled Replay, a novel replay strategy for Continual Learning which is able to mitigate forgetting by keeping a very small buffer (1 pattern per class) of highly informative samples. Distilled Replay builds the buffer through a distillation process which compresses a large dataset into a tiny set of informative examples. We show the effectiveness of our Distilled Replay against popular replay-based strategies on four Continual Learning benchmarks.
Validity and Reliability of Student Models for Problem-Solving Activities

Student models are typically evaluated through predicting the correctness of the next answer. This approach is insufficient in the problem-solving context, especially for student models that use performance data beyond binary correctness. We propose more comprehensive methods for validating student models and illustrate them in the context of introductory programming. We demonstrate the insufficiency of the next answer correctness prediction task, as it is neither able to reveal low validity of student models that use just binary correctness, nor does it show increased validity of models that use other performance data. The key message is that the prevalent usage of the next answer correctness for validating student models and binary correctness as the only input to the models is not always warranted and limits the progress in learning analytics.