Reviewerly/exHarmony huggingface dataset:
Exploration for Educational Application of Metaverse: Focusing on Implication for Use in English Education

Purpose: As the non-face-to-face situation triggered by COVID-19 continues for a long time, the desire to experience a sense of reality and realism even through an online virtual environment is increasing, and the metaverse is emerging as an alternative in the field of education. Therefore, the purpose of this study is to examine the meaning of metaverse, which has become a hot topic in recent years, from an educational point of view and to suggest the possibility of its application in English education. Method: The social and cultural paradigm of the 4th industrial revolution has brought many changes to the education field and is now expanding into a new area with the metaverse. In this study, what kinds of studies and use cases are related to the recent metaverse in Korea, and what are the types and characteristics of the metaverse that are mainly used. In addition, I would like to examine the characteristics that appear in the application process of educational activities using the metaverse reported in this study, how these characteristics can be grafted into English and teaching and learning activities, and what advantages there can be. Results: As a result of analyzing this from the educational aspect, positive factors of metaverse such as an increase in the educational presence and class participation, alleviation of anxiety, immersive learning, and interest and motivation were selected, focusing on the implications for use in English education. It showed potential for use as a tool. As such, it is judged that the metaverse can play an important role at the center of the future English education that is rapidly changing due to the non-face-to-face situation and the 4th industrial revolution technology. Conclusion: The metaverse can overcome the sense of physical distance, and if the existing metaverse platform is used to the maximum to configure a space suitable for the purpose of learning and provide content, a qualita-tive teaching and learning space can be provided. In the future, based on this study, we expect various types of empirical research to examine the effectiveness by selecting a metaverse platform suitable for the purpose and target of English class and designing an actual class space.
Is GitHub's Copilot as Bad as Humans at Introducing Vulnerabilities in Code?

Several advances in deep learning have been successfully applied to the software development process. Of recent interest is the use of neural language models to build tools, such as Copilot, that assist in writing code. In this paper we perform a comparative empirical analysis of Copilot-generated code from a security perspective. The aim of this study is to determine if Copilot is as bad as human developers. We investigate whether Copilot is just as likely to introduce the same software vulnerabilities as human developers. Using a dataset of C/C++ vulnerabilities, we prompt Copilot to generate suggestions in scenarios that led to the introduction of vulnerabilities by human developers. The suggestions are inspected and categorized in a 2-stage process based on whether the original vulnerability or fix is reintroduced. We find that Copilot replicates the original vulnerable code about 33% of the time while replicating the fixed code at a 25% rate. However this behaviour is not consistent: Copilot is more likely to introduce some types of vulnerabilities than others and is also more likely to generate vulnerable code in response to prompts that correspond to older vulnerabilities. Overall, given that in a significant number of cases it did not replicate the vulnerabilities previously introduced by human developers, we conclude that Copilot, despite performing differently across various vulnerability types, is not as bad as human developers at introducing vulnerabilities in code.
[Distribution Characteristics of Heavy Metals in Soils Affected by Different Land Use Types in a Superimposed Pollution Area with High Geological Background].

The high geological background and the residual pollution of historical zinc smelting in the karst area of northwest Guizhou has led to serious soil heavy metal pollution. In order to understand the characteristics of soil heavy metal pollution in agricultural land, wasteland, and forestland, 126 surface and profile soil samples were collected from a typical zinc powder factory-polluted small watershed in Weining County, Northwest Guizhou Province. The concentrations and distribution characteristics of Cd, As, Pb, Cu, and Zn in soils were studied. The results showed that the average ω(Cd), ω(As), ω(Pb), ω(Cu), and ω(Zn) in the surface soil were 8.14, 45.07, 263.97, 41.36, and 716.98 mg·kg-1, respectively, which were 1.29-12.33 times higher than the background values. The content of heavy metals in soil of different land uses in descending order was agricultural land, wasteland, and forestland. From the perspective of spatial distribution, the soil with high heavy metal concentration was concentrated around the zinc smelting plant and slag, and the accumulation of heavy metals in soil was greatly affected by the pollution sources of mining activities but less affected by the land use mode. In soil profile, heavy metals in the surface layer were generally higher than those in bottom layers, showing a significant difference (P<0.05) in wasteland. Five heavy metals in 80-100 cm soil were higher than the background value, reflecting the existence of a high geological background value. Correlation analysis and principal component analysis showed that Pb, Zn, and As in the surface soil were mainly affected by human activities such as mining and transportation, whereas Cd and Cu were both affected by high geological background and human activities. The single ecological risk coefficient of Cd in soil was the highest, which reached very strong or very strong risk, whereas As, Zn, Cu, and Pb reached slight or medium risk. The comprehensive potential ecological risk index indicated strong ecological risk, which in agricultural land was higher than that in wasteland and forestland. In the karst high geological background area, the influence of lead and zinc smelting on the distribution and accumulation of heavy metals in soils was greater than that of land use; soil heavy metal pollution was serious, and ecological risk was high in this area. Therefore, strict control of heavy metal-contaminated soil should be strengthened.
Improving Rare Word Recognition with LM-aware MWER Training

Language models (LMs) significantly improve the recognition accuracy of end-to-end (E2E) models on words rarely seen during training, when used in either the shallow fusion or the rescoring setups. In this work, we introduce LMs in the learning of hybrid autoregressive transducer (HAT) models in the discriminative training framework, to mitigate the training versus inference gap regarding the use of LMs. For the shallow fusion setup, we use LMs during both hypotheses generation and loss computation, and the LM-aware MWER-trained model achieves 10\% relative improvement over the model trained with standard MWER on voice search test sets containing rare words. For the rescoring setup, we learn a small neural module to generate per-token fusion weights in a data-dependent manner. This model achieves the same rescoring WER as regular MWER-trained model, but without the need for sweeping fusion weights.
Understanding glioblastoma invasion using physically-guided neural networks with internal variables

Microfluidic capacities for both recreating and monitoring cell cultures have opened the door to the use of Data Science and Machine Learning tools for understanding and simulating tumor evolution under controlled conditions. In this work, we show how these techniques could be applied to study Glioblastoma, the deadliest and most frequent primary brain tumor. In particular, we study Glioblastoma invasion using the recent concept of Physically-Guided Neural Networks with Internal Variables (PGNNIV), able to combine data obtained from microfluidic devices and some physical knowledge governing the tumor evolution. The physics is introduced in the network structure by means of a nonlinear advection-diffusion-reaction partial differential equation that models the Glioblastoma evolution. On the other hand, multilayer perceptrons combined with a nodal deconvolution technique are used for learning the go or grow metabolic behavior which characterises the Glioblastoma invasion. The PGNNIV is here trained using synthetic data obtained from in silico tests created under different oxygenation conditions, using a previously validated model. The unravelling capacity of PGNNIV enables discovering complex metabolic processes in a non-parametric way, thus giving explanatory capacity to the networks, and, as a consequence, surpassing the predictive power of any parametric approach and for any kind of stimulus. Besides, the possibility of working, for a particular tumor, with different boundary and initial conditions, permits the use of PGNNIV for defining virtual therapies and for drug design, thus making the first steps towards in silico personalised medicine.
Sentiment analysis with ensemble and machine learning methods in multi-domain datasets

The first place to get ideas on all the activities considered to occur in everyday life was the comments on the websites. This is an area that deals with these interpretations in the natural language processing, which is a sub-branch of artificial intelligence. Sentiment analysis studies, which is a task of natural language processing are carried out to give people an idea and even guide them with such comments. In this study, sentiment analysis was implemented on public user feedback on websites in two different areas. TripAdvisor dataset includes positive or negative user comments about hotels. And Rotten Tomatoes dataset includes positive (fresh) or negative (rotten) user comments about films. Sentiments analysis on datasets have been carried out by using Word2Vec word embedding model, which learns the vector representations of each word containing the positive or negative meaning of the sentences, and the Term Frequency Inverse Document Frequency text representation model with four machine learning methods (Naïve Bayes-NB, Support Vector Machines-SVM, Logistic Regression-LR, K-Nearest Neighbour-kNN) and two ensemble learning methods (Stacking, Majority Voting-MV). Accuracy and F-measure is used as a performance metric experiments. According to the results, Ensemble learning methods have shown better results than single machine learning algorithms. Among the overall approaches, MV outperformed Stacking.
A tunable quantum random number generator based on a fiber-optical Sagnac interferometer

Abstract Quantum random number generators (QRNGs) are based on naturally random measurement results performed on individual quantum systems. Here, we demonstrate a branching-path photonic QRNG implemented using a Sagnac interferometer with a tunable splitting ratio. The fine-tuning of the splitting ratio allows us to maximize the entropy of the generated sequence of random numbers and effectively compensate for tolerances in the components. By producing single-photons from attenuated telecom laser pulses, and employing commercially-available components we are able to generate a sequence of more than 2 gigabytes of random numbers with an average entropy of 7.99 bits/byte directly from the raw measured data. Furthermore, our sequence passes randomness tests from both the NIST and Dieharder statistical test suites, thus certifying its randomness. Our scheme shows an alternative design of QRNGs based on the dynamic adjustment of the uniformity of the produced random sequence, which is relevant for the construction of modern generators that rely on independent real-time testing of its performance.
Infographics Wizard: Flexible Infographics Authoring and Design Exploration

Abstract Infographics are an aesthetic visual representation of information following specific design principles of human perception. Designing infographics can be a tedious process for non‐experts and time‐consuming, even for professional designers. With the help of designers, we propose a semi‐automated infographic framework for general structured and flow‐based infographic design generation. For novice designers, our framework automatically creates and ranks infographic designs for a user‐provided text with no requirement for design input. However, expert designers can still provide custom design inputs to customize the infographics. We will also contribute an individual visual group (VG) designs dataset (in SVG), along with a 1k complete infographic image dataset with segmented VGs in this work. Evaluation results confirm that by using our framework, designers from all expertise levels can generate generic infographic designs faster than existing methods while maintaining the same quality as hand‐designed infographics templates.
From attention to action: Key drivers to augment VR experience for everyday consumer applications

The rapid growth of AR/VR/XR technology resulted in development of various applications for branding, advertising and commerce. However, understanding consumer demands, and brand consumer-dynamics in creating immersive and engaging experiences is still a challenge. Part of the challenge reflects the lack of understanding on the fundamental processes underlying human behaviour (from attention to action) between virtual and real worlds. Note also that marketing in real worlds and in virtual worlds may differ, and thus, fostering reconsideration of existing VR theories and marketing strategies. The present study addresses the above challenges and aims at providing better understanding on how to augment AR/VR/XR experience for everyday consumer applications. In particular, multisensory processing and social interactions will be addressed in rendering VR experience that is much more immersive and engaging for the user, and thus, being a prerequisite for enhanced consumer journey. We suggest a framework encompassing the key determinants from attention to action, and thus, providing understanding on how to augment experience. Implementing the conceptual framework offers innovative ways for brands to reach, attract, and retain customers via appealing multisensory experiences enhancing the brand portfolio beyond the traditional shopping environment.
AiRType: An Air-tapping Keyboard for Augmented Reality Environments

We present AiRType for AR/VR HMDs that enables text entry through bare hands for more natural perception. The hand models in the virtual environment mirror hand movements of the user and user targets and selects the keys via hand models. AiRType fully leverages the additional dimension without restraining the interaction space by users&#x0027; arm lengths. It can be attached to anywhere and can be scaled freely. We evaluated and compared AiRType with the baseline-the built-in keyboard of Magic Leap 1. AiRType shows 27&#x0025; decrease in the error rate, 3.3&#x0025; increase in character-per-second, and 9.4&#x0025; increase in user satisfaction.
Standing on the Shoulders of Giant Frozen Language Models

Huge pretrained language models (LMs) have demonstrated surprisingly good zero-shot capabilities on a wide variety of tasks. This gives rise to the appealing vision of a single, versatile model with a wide range of functionalities across disparate applications. However, current leading techniques for leveraging a "frozen" LM -- i.e., leaving its weights untouched -- still often underperform fine-tuning approaches which modify these weights in a task-dependent way. Those, in turn, suffer forgetfulness and compromise versatility, suggesting a tradeoff between performance and versatility. The main message of this paper is that current frozen-model techniques such as prompt tuning are only the tip of the iceberg, and more powerful methods for leveraging frozen LMs can do just as well as fine tuning in challenging domains without sacrificing the underlying model's versatility. To demonstrate this, we introduce three novel methods for leveraging frozen models: input-dependent prompt tuning, frozen readers, and recursive LMs, each of which vastly improves on current frozen-model approaches. Indeed, some of our methods even outperform fine-tuning approaches in domains currently dominated by the latter. The computational cost of each method is higher than that of existing frozen model methods, but still negligible relative to a single pass through a huge frozen LM. Each of these methods constitutes a meaningful contribution in its own right, but by presenting these contributions together we aim to convince the reader of a broader message that goes beyond the details of any given method: that frozen models have untapped potential and that fine-tuning is often unnecessary.
SeCaV: A Sequent Calculus Verifier in Isabelle/HOL

We describe SeCaV, a sequent calculus verifier for first-order logic in Isabelle/HOL, and the SeCaV Unshortener, an online tool that expands succinct derivations into the full SeCaV syntax.We leverage the power of Isabelle/HOL as a proof checker for our SeCaV derivations.The interactive features of Isabelle/HOL make our system transparent.For instance, the user can simply click on a side condition to see its exact definition.Our formalized soundness and completeness proofs pertain exactly to the calculus as exposed to the user and not just to some model of our tool.Users can also write their derivations in the SeCaV Unshortener, which provides a lighter syntax, and expand them for later verification.We have used both tools in our teaching.
Sub‐region non‐local mean denoising algorithm of synthetic aperture radar images based on statistical characteristics

When synthetic aperture radar (SAR) images are denoised by non-local mean (NLM) algorithm, logarithmic transformation will lead to the loss of some image information. To keep the details and smooth the noise of the SAR images better, a new sub-region NLM denoising algorithm with the statistical characteristics of SAR image is proposed in this paper. Firstly, the probability distribution image is generated by calculating the probability value of every pixel. Then the images can be divided into the heterogeneous region and the homogeneous region by the threshold obtained with the variation coefficient of the probability image. A new filtering weight using both the original and probability images is generated based on NLM in the heterogeneous region. The filtering weight is obtained using the probability image in the homogeneous region. This method fully considers the characteristics of noise in different regions. Multi-SAR image experiments demonstrate the advantages of noise smooth and detail protection.
Towards Measuring Domain Shift in Histopathological Stain Translation in an Unsupervised Manner

Domain shift in digital histopathology can occur when different stains or scanners are used, during stain translation, etc. A deep neural network trained on source data may not generalise well to data that has undergone some domain shift. An important step towards being robust to domain shift is the ability to detect and measure it. This article demonstrates that the PixelCNN and domain shift metric can be used to detect and quantify domain shift in digital histopathology, and they demonstrate a strong correlation with generalisation performance. These findings pave the way for a mechanism to infer the average performance of a model (trained on source data) on unseen and unlabelled target data.
EP-PQM: Efficient Parametric Probabilistic Quantum Memory With Fewer Qubits and Gates

Machine learning (ML) classification tasks can be carried out on a quantum computer (QC) using probabilistic quantum memory (PQM) and its extension, parametric PQM (P-PQM), by calculating the Hamming distance between an input pattern and a database of r patterns containing z features with a distinct attributes.For PQM and P-PQM to correctly compute the Hamming distance, the feature must be encoded using one-hot encoding, which is memory intensive for multiattribute datasets with a > 2. We can represent multiattribute data more compactly by replacing one-hot encoding with label encoding; both encodings yield the same Hamming distance.Implementing this replacement on a classical computer is trivial.However, replacing these encoding schemes on a QC is not straightforward because PQM and P-PQM operate at the bit level, rather than at the feature level (a feature is represented by a binary string of 0's and 1's).We present an enhanced P-PQM, called efficient P-PQM (EP-PQM), that allows label encoding of data stored in a PQM data structure and reduces the circuit depth of the data storage and retrieval procedures.We show implementations for an ideal QC and a noisy intermediate-scale quantum (NISQ) device.Our complexity analysis shows that the EP-PQM approach requires O(z log 2 (a)) qubits as opposed to O(za) qubits for P-PQM.EP-PQM also requires fewer gates, reducing gate count from O(rza) to O(rz log 2 (a)).For five datasets, we demonstrate that training an ML classification model using EP-PQM requires 48% to 77% fewer qubits than P-PQM for datasets with a > 2. EP-PQM reduces circuit depth in the range of 60% to 96%, depending on the dataset.The depth decreases further with a decomposed circuit, ranging between 94% and 99%.EP-PQM requires less space; thus, it can train on and classify larger datasets than previous PQM implementations on NISQ devices.Furthermore, reducing the number of gates speeds up the classification and reduces the noise associated with deep quantum circuits.Thus, EP-PQM brings us closer to scalable ML on an NISQ device.
Tisane: Authoring Statistical Models via Formal Reasoning from Conceptual and Data Relationships

Proper statistical modeling incorporates domain theory about how concepts relate and details of how data were measured. However, data analysts currently lack tool support for recording and reasoning about domain assumptions, data collection, and modeling choices in an integrated manner, leading to mistakes that can compromise scientific validity. For instance, generalized linear mixed-effects models (GLMMs) help answer complex research questions, but omitting random effects impairs the generalizability of results. To address this need, we present Tisane, a mixed-initiative system for authoring generalized linear models with and without mixed-effects. Tisane introduces a study design specification language for expressing and asking questions about relationships between variables. Tisane contributes an interactive compilation process that represents relationships in a graph, infers candidate statistical models, and asks follow-up questions to disambiguate user queries to construct a valid model. In case studies with three researchers, we find that Tisane helps them focus on their goals and assumptions while avoiding past mistakes.
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

Cross-entropy loss and focal loss are the most common choices when training deep neural networks for classification problems. Generally speaking, however, a good loss function can take on much more flexible forms, and should be tailored for different tasks and datasets. Motivated by how functions can be approximated via Taylor expansion, we propose a simple framework, named PolyLoss, to view and design loss functions as a linear combination of polynomial functions. Our PolyLoss allows the importance of different polynomial bases to be easily adjusted depending on the targeting tasks and datasets, while naturally subsuming the aforementioned cross-entropy loss and focal loss as special cases. Extensive experimental results show that the optimal choice within the PolyLoss is indeed dependent on the task and dataset. Simply by introducing one extra hyperparameter and adding one line of code, our Poly-1 formulation outperforms the cross-entropy loss and focal loss on 2D image classification, instance segmentation, object detection, and 3D object detection tasks, sometimes by a large margin.