Reviewerly/exHarmony huggingface dataset:
A Systematic Review of the Challenges and Solutions of ESL Students’ Academic Writing

Research studies on ESL writing are more interested in providing operational writing strategies for academic writing. However, there are not many studies on challenges faced by students and their solutions. The main aim of this paper is to provide a systematic literature review of academic writing studies which focused on investigating students’ academic writing challenges and their solutions. The papers used in this study were published from 2010 until 2019. A systematic search of literature proposed in this paper employs the exploratory approach for identifying and evaluating twenty-seven articles published in the authentic Journals. The themes of the review are divided into two categories, one which is related to ESL Malaysian students and another category is regarding non-Malaysians, this is due to the context of the study which will be conducted in Malaysia and therefore a review of studies done on Malaysian student academic writing is justified. The review shows there is a broader context of ESL students’ academic writing challenges and factors influencing students’ academic writing skills. In addition, as mentioned above, the review clearly shows the highlight of previous studies was on strategies which help improve students writing skills. As a conclusion, the review signifies that there are three major enormous challenges faced by ESL students in academic writing, such as teaching methods, students’ attitude towards English, and language ability. And the solutions to the challenges are to employ active learning English language teaching methods, for example, Task-Based learning (TBL), Blended learning, Collaborative learning and cognitive approach, in teaching writing.
On Unmanned Aerial Vehicles Light Show Systems: Algorithms, Software and Hardware

Unmanned aerial vehicle (UAV) light shows (UAV-LS) have a wow factor due to their advantages in terms of environment friendliness and controllability compared to traditional fireworks. In this paper, a UAV-LS system is developed including a collision-free formation transformation trajectory planning algorithm, a software package that facilitates animation design and real-time monitoring and control, and hardware design and realization. In particular, a dynamic task assignment algorithm based on graph theory is proposed to reduce the impact of UAV collision avoidance on task assignment and the frequency of task assignment in the formation transformation. In addition, the software package consists of an animation interface for formation drawing and 3D animation simulation, which helps the monitoring and control of UAVs through a real-time monitoring application. The developed UAV-LS system hardware consists of subsystems of decision-making, real-time kinematic (RTK) global positioning system (GPS), wireless communication, and UAV platforms. Outdoor experiments using six quadrotors are performed and details of implementations of high-accuracy positioning, communication, and computation are presented. Results show that the developed UAV-LS system can successfully complete a light show and the proposed task assignment algorithm performs better than traditional static ones.
A 3d Model-Based Approach For Fitting Masks To Faces In The Wild

Face recognition now requires a large number of labelled masked face images in the era of this unprecedented COVID19 pandemic. Unfortunately, the rapid spread of the virus has left us little time to prepare for such dataset in the wild. To circumvent this issue, we present a 3D model-based approach called WearMask3D for augmenting face images of various poses to the masked face counterparts. Our method proceeds by first fitting a 3D morphable model on the input image, second overlaying the mask surface onto the face model and warping the respective mask texture, and last projecting the 3D mask back to 2D. The mask texture is adapted based on the brightness and resolution of the input image. By working in 3D, our method can produce more natural masked faces of diverse poses from a single mask texture. To compare precisely between different augmentation approaches, we have constructed a dataset comprising masked and unmasked faces with labels called MFW-mini. Experimental results demonstrate WearMask3D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> produces more realistic masked faces, and utilizing these images for training leads to state-of-the-art recognition accuracy for masked faces.
Bosonic field digitization for quantum computers

Quantum simulation of quantum field theory is a flagship application of quantum computers that promises to deliver capabilities beyond classical computing. The realization of quantum advantage will require methods to accurately predict error scaling as a function of the resolution and parameters of the model that can be implemented efficiently on quantum hardware. In this paper, we address the representation of lattice bosonic fields in a discretized field amplitude basis, develop methods to predict error scaling, and present efficient qubit implementation strategies. A low-energy subspace of the bosonic Hilbert space, defined by a boson occupation cutoff, can be represented with exponentially good accuracy by a low-energy subspace of a finite size Hilbert space. The finite representation construction and the associated errors are directly related to the accuracy of the Nyquist-Shannon sampling and the Finite Fourier transforms of the boson number states in the field and the conjugate-field bases. We analyze the relation between the boson mass, the discretization parameters used for wavefunction sampling and the finite representation size. Numerical simulations of small size $\Phi^4$ problems demonstrate that the boson mass optimizing the sampling of the ground state wavefunction is a good approximation to the optimal boson mass yielding the minimum low-energy subspace size. However, we find that accurate sampling of general wavefunctions does not necessarily result in accurate representation. We develop methods for validating and adjusting the discretization parameters to achieve more accurate simulations.
Named Entity Recognition in Equipment Support Field Using Tri-Training Algorithm and Text Information Extraction Technology

NER (Named Entity Recognition) is of great significance for the construction of a knowledge map. The purpose is to guarantee the recognition effect of named entity recognition method in the application scenario of vertical field, a named entity recognition method is proposed based on BI-LSTM-CRF [BI(Bidirectional) LSTM (Long-Short Term Memory) CRF (Conditional Random Field)] for equipment support field, which improves the recognition effect of the domain named entity and provides technical support for the subsequent construction of domain knowledge map. First, Chinese characters are represented by word embedding and input into the model. Then, the input feature vector sequence is processed through BI-LSTM NN (Neural Network) to extract contextual semantic learning features. Finally, the learned features are connected to the linear CRF, the NEs (Named Entity) in the field of equipment support are labeled, and the NER results are obtained and output. The experimental results show that the precision of the named entity recognition method based on the BI-LSTM-CRF model has reached 92.02%, the recall rate has reached 93.21%, and the F1score has reached 93.88%. Meanwhile, the performance of the proposed BI-LSTM-CRF model is higher than the precision of the BI-LSTM NN model and LSTM-CRF NN model.
GPU-Accelerated Constraint-Based Causal Structure Learning for Discrete Data

Learning the causal structures from high-dimensional observational data is an omnipresent challenge in data science.State-of-the-art methods for constraint-based Causal Structure Learning (CSL) apply conditional independence (CI) tests to determine the underlying causal structures.In the context of discrete data, each CI test requires calculating the marginals over contingency tables based on the respective observations.This calculation leads to long overall execution times.In our work, we propose a parallel execution strategy tailored for constraint-based CSL on a Graphics Processing Unit (GPU) to accelerate the execution for discrete data.Hence, we introduce the gpuPC algorithm that performs all CI tests on a GPU and extends the existing parallel execution strategy for constraint-based CSL by calculating the marginals over contingency tables within units of threads.Further, gpuPC implements explicit memory management to handle the corresponding auxiliary data structures in GPU memory.An experimental evaluation shows that gpuPC scales well even for higher-dimensional settings, with auxiliary data structures exceeding on-chip memory.In particular, running on NVIDIA Tesla V100 hardware gpuPC outperforms a GPU baseline by factors of up to 45.6 and further outperforms existing parallel CPU-based implementations running on 40 cores by a factor of 62.1.
Deep Saliency Prior for Reducing Visual Distraction

Using only a model that was trained to predict where people look at images, and no additional training data, we can produce a range of powerful editing effects for reducing distraction in images. Given an image and a mask specifying the region to edit, we backpropagate through a state-of-the-art saliency model to parameterize a differentiable editing operator, such that the saliency within the masked region is reduced. We demonstrate several operators, including: a recoloring operator, which learns to apply a color transform that camouflages and blends distractors into their surroundings; a warping operator, which warps less salient image regions to cover distractors, gradually collapsing objects into themselves and effectively removing them (an effect akin to inpainting); a GAN operator, which uses a semantic prior to fully replace image regions with plausible, less salient alternatives. The resulting effects are consistent with cognitive research on the human visual system (e.g., since color mismatch is salient, the recoloring operator learns to harmonize objects' colors with their surrounding to reduce their saliency). And importantly, all effects are achieved under a zero-shot learning scenario, solely through the guidance of the pretrained saliency model, with no supervised data of the effects. We present results on a variety of natural images and conduct a perceptual study to evaluate and validate the changes in viewers' eye-gaze between the original images and our edited results. Project Webpage: https://deep-saliency-prior.github.io/
Strong Depth Relevance

Relevant logics infamously have the property that they only validate a conditional when some propositional variable is shared between its antecedent and consequent. This property has been strengthened in a variety of ways over the last half-century. Two of the more famous of these strengthenings are the strong variable sharing property and the depth relevance property. In this paper I demonstrate that an appropriate class of relevant logics has a property that might naturally be characterized as the supremum of these two properties. I also show how to use this fact to demonstrate that these logics seem to be constructive in previously unknown ways.
Prospects for Using the Digital Footprint in Educational and Scientific Processes

Introduction. Analysis of the digital footprint of a student and / or a researcher is a promising area for optimizing the individual development path. Materials and Methods. Various approaches to the interpretation and use of the digital footprint are considered. A model of the use of a trace is given, depending on the didactic task being solved, the choice of which determines the use of a specific digital tool from the cloud. The work uses various methods: analysis of articles and publications on the selected topic, systematization of the knowledge gained, classification and interpretation of terms and concepts. Results. The authors considered the features of the analysis of digital traces in such environments, programs, applications, websites, electronic resources such as a scientific electronic library, a search query service, social networks, international databases of scientific publications, citation services, services for managing joint projects, websites for Internet seminars (webinars), Moodle learning environment, video hosting, etc. Discussion and Conclusions. The study of digital footprints is a promising area. The advantages of this approach include: the ability to declare oneself as a research scientist; active search for like-minded people on the Internet; understanding the area of scientific interest; the possibility of receiving feedback, incl. advice from the professional community.
Common Gabor Features for Image Watermarking Identification

Image watermarking is one of many methods for preventing unauthorized alterations to digital images. The major goal of the research is to find and identify photos that include a watermark, regardless of the method used to add the watermark or the shape of the watermark. As a result, this study advocated using the best Gabor features and classifiers to improve the accuracy of image watermarking identification. As classifiers, discriminant analysis (DA) and random forests are used. The DA and random forest use mean squared energy feature, mean amplitude feature, and combined feature vector as inputs for classification. The performance of the classifiers is evaluated using a variety of feature sets, and the best results are achieved. In order to assess the performance of the proposed method, we use a public database. VOC2008 is a public database that we use. The findings reveal that our proposed method’s DA classifier with integrated features had the greatest TPR of 93.71 and the lowest FNR of 6.29. This shows that the performance outcomes of the proposed approach are consistent. The proposed method has the advantages of being able to find images with the watermark in any database and not requiring a specific type or algorithm for embedding the watermark.
A Study on growth in Technology and Innovation across the globe in the Field of Education and Business

Today, we are witnessing an immense digital transformation across the globe.Technology is accelerating innovations, agility, and business growth.Businesses are redefining their operations by incorporating types of technologies like Artificial Intelligence, Blockchain, Machine Learning and Cloud Computing.Education is one of the unattended segments which can be lifted with the help of technology adoption.Higher Education institutions will accelerate the adoption of cloud-based technologies for Student Relationship Management (CRM), Learning Management Systems (LMS), Assessment Management, etc.The Covid-19 pandemic persuaded a rapid digitization and automation which enabled businesses and Education to be flexible even in a time of crisis.Many enterprises adopted disruptive technology and changed their business models to keepup with the changing pace.The impact created by the pandemic will not fade away and the digital shift remain forever.Moving with the imperfection will be difficult if companies or Education Institutions not adopting the new technology trends.This study through light on some of the trending technologies in 2021 that need to be follow and adopt with businesses and Education Institutions in the current pandemic senario.Advanced technology will continue to transform our lives and work by creating big opportunities for Education Institutions and industries worldwide.
Effect of noisy channels on the transmission of mesoscopic twin-beam states

Quantum properties of light, which are crucial resources for quantum technologies, are quite fragile in nature and can be degraded and even concealed by the environment. We show, both theoretically and experimentally, that mesoscopic twin-beam states of light can preserve their nonclassicality even in the presence of major losses and different types of noise, thus suggesting their potential usefulness to encode information in quantum communication protocols. We develop a comprehensive general analytical model for a measurable nonclassicality criterion and find thresholds on noise and losses for the survival of entanglement in the twin beam.
On the impact of smart contracts on auditing

The use of smart contracts has grown exponentially over the last few years. This is a phenomenon associated with the development of other technologies, such as the blockchain and the Internet of Things (IoT). Smart contracts run in a decentralized way on the blockchain and are self- executing. This is a source of advantages in business operations, but there are also some limitations and drawbacks. Regulatory issues are also of key importance, as the legal frameworks differ across countries. Smart contracts are likely to have an impact on external auditing, as external auditors will have to adapt their capabilities and procedures to an environment where many companies use this technology. But smart contracts may also be used to define a framework which ensures continuous audit reports and direct access of authorized stakeholders to the results of audit procedures. Conversely, internal auditing will also experiment changes, both caused by a series of new risks that will have to be adequately addressed and new tools to monitor business operations. In addition, some promising research opportunities arise, both in the IT, the Legal and the Business field.
Fair Voice Biometrics: Impact of Demographic Imbalance on Group Fairness in Speaker Recognition

Speaker recognition systems are playing a key role in modern online applications.Though the susceptibility of these systems to discrimination according to group fairness metrics has been recently studied, their assessment has been mainly focused on the difference in equal error rate across groups, not accounting for other fairness criteria important in anti-discrimination policies, defined for demographic groups characterized by sensitive attributes.In this paper, we therefore study how existing group fairness metrics relate with the balancing settings of the training data set in speaker recognition.We conduct this analysis by operationalizing several definitions of fairness and monitoring them under varied data balancing settings.Experiments performed on three deep neural architectures, evaluated on a data set including gender/age-based groups, show that balancing group representation positively impacts on fairness and that the friction across security, usability, and fairness depends on the fairness metric and the recognition threshold.
Students’ Digital Literacy Competence and English Study Habits

Digital literacy competence is an excellent choice as it promotes a more exciting and contextual instruction process.This research explores and reveals the students' profile of digital literacy competence and English learning habits.This study surveyed 402 students from Universitas Katolik Indonesia Santu Paulus Ruteng.A questionnaire was used to collect data, which was then quantitatively and qualitatively analyzed.According to the findings, students' digital literacy competence included in the medium category (3,76).Three main digital competencies indicated this category; students' information literacy competence was in the medium category (3,9), media was in the medium category (3,6), and ICT competence was in the medium category (3,78).English learning habits were also classified as high level, as shown by six learning habits: a review of the material, 77,61%, self-evaluation, 92,29%, summaries the material, 80,10%, do exercises, 73,88%, map priority, 88,56%, and have a fixed schedule, 81,59%.
A Multimodal Model for Predicting Conversational Feedbacks

We propose in this paper a statistical model in the perspective of predicting listener’s feedbacks in a conversation. The first contribution of the paper is a study of the prediction of all feedbacks, including those in overlap with the speaker with a good accuracy. Existing model are good at predicting feedbacks during a pause, but reach a very low success level for all feedbacks. We give in this paper a first step towards this complex problem. The second contribution is a model predicting precisely the type of the feedback (generic vs. specific) as well as other specific features (valence expectation) useful in particular for generating feedbacks in dialogue systems. This work relies on an original corpus.
On the experiment-friendly formulation of quantum backflow

In its standard formulation, quantum backflow is a classically impossible phenomenon in which a free quantum particle in a positive-momentum state exhibits a negative probability current. Recently, Miller et al. [Quantum 5, 379 (2021)] have put forward a new, "experiment-friendly" formulation of quantum backflow that aims at extending the notion of quantum backflow to situations in which the particle's state may have both positive and negative momenta. Here, we investigate how the experiment-friendly formulation of quantum backflow compares to the standard one when applied to a free particle in a positive-momentum state. We show that the two formulations are not always compatible. We further identify a parametric regime in which the two formulations appear to be in qualitative agreement with one another.
A Privacy-Preserving Data Transmission Scheme Based on Oblivious Transfer and Blockchain Technology in the Smart Healthcare

With the development of the Internet of Things and the demand for telemedicine, the smart healthcare system has attracted much attention in recent years. As a platform for medical data interaction, the smart healthcare system is demanded to ensure the privacy of both the receiver and the sender, as well as the security of data transmission. In this paper, we propose a privacy-preserving data transmission scheme where both secure ciphertext conversion and malicious users identification are supported. In particular, the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msubsup> <mrow> <mfenced open="(" close=")" separators="|"> <mrow> <mtext>OT</mtext> </mrow> </mfenced> </mrow> <mi>m</mi> <mi>n</mi> </msubsup> </math> protocol is introduced to guarantee the two-way privacy of communication parties. Meanwhile, we adopt proxy reencryption algorithm to support secure ciphertext conversion so as to ensure the confidentiality of data in many-to-many communication pattern. In addition, by taking advantage of the concept of blockchain technology, a novel <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <msubsup> <mrow> <mfenced open="(" close=")" separators="|"> <mrow> <mtext>OT</mtext> </mrow> </mfenced> </mrow> <mi>m</mi> <mi>n</mi> </msubsup> </math> protocol is proposed to prevent data from being tampered with and effectively identify malicious users. Theoretical and experimental analyses indicate that the proposed scheme is practical for smart healthcare with high security and efficiency.
Avoiding Inference Heuristics in Few-shot Prompt-based Finetuning

Recent prompt-based approaches allow pretrained language models to achieve strong performances on few-shot finetuning by reformulating downstream tasks as a language modeling problem.In this work, we demonstrate that, despite its advantages on low data regimes, finetuned prompt-based models for sentence pair classification tasks still suffer from a common pitfall of adopting inference heuristics based on lexical overlap, e.g., models incorrectly assuming a sentence pair is of the same meaning because they consist of the same set of words.Interestingly, we find that this particular inference heuristic is significantly less present in the zero-shot evaluation of the prompt-based model, indicating how finetuning can be destructive to useful knowledge learned during the pretraining.We then show that adding a regularization that preserves pretraining weights is effective in mitigating this destructive tendency of few-shot finetuning.Our evaluation on three datasets demonstrates promising improvements on the three corresponding challenge datasets used to diagnose the inference heuristics. 1