Reviewerly/exHarmony huggingface dataset:
Racing trees to query partial data

Dealing with partially known or missing data is a common problem in machine learning. This work is interested in the problem of querying the true value of data to improve the quality of the learned model, when those data are only partially known. This study is in the line of active learning, since we consider that the precise value of some partial data can be queried to reduce the uncertainty in the learning process, yet can consider any kind of partial data (not only entirely missing one). We propose a querying strategy based on the concept of racing algorithms in which several models are competing. The idea is to identify the query that will help the most to quickly decide the winning model in the competition. After discussing and formalizing the general ideas of our approach, we study the particular case of decision trees in case of interval-valued features and set-valued labels. The experimental results indicate that, in comparison with other baselines, the proposed approach significantly outperforms simpler strategies in the case of partially specified features, while it achieves similar performances in the case of partially specified labels.
Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition

Named entity recognition (NER) is a well-studied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The span-based methods treat entity recognition as a span classification task. Although these methods have the innate ability to handle nested NER, they suffer from high computational cost, ignorance of boundary information, under-utilization of the spans that partially match with entities, and difficulties in long entity recognition. To tackle these issues, we propose a two-stage entity identifier. First we generate span proposals by filtering and boundary regression on the seed spans to locate the entities, and then label the boundary-adjusted span proposals with the corresponding categories. Our method effectively utilizes the boundary information of entities and partially matched spans during training. Through boundary regression, entities of any length can be covered theoretically, which improves the ability to recognize long entities. In addition, many low-quality seed spans are filtered out in the first stage, which reduces the time complexity of inference. Experiments on nested NER datasets demonstrate that our proposed method outperforms previous state-of-the-art models.
New hemisystems of the Hermitian surface

Constructing hemisystems of the Hermitian surface is a well known, apparently difficult, problem in Finite geometry. So far, a few infinite families and some sporadic examples have been constructed. One of the different approaches relies on the Fuhrmann-Torres maximal curve and provides a hemisystem in $$PG(3,p^2)$$ for every prime p of the form $$p=1+4a^2$$ , a even. Here we show that this approach also works in $$PG(3,p^2)$$ for every prime $$p=1+4a^2$$ , a odd. The resulting hemisystem gives rise to two weight linear codes and strongly regular graphs whose properties are also investigated.
Parietal and hippocampal hyper-connectivity is associated with low math achievement in adolescence – a preliminary study

Mathematical cognition requires coordinated activity across multiple brain regions, leading to the emergence of resting-state functional connectivity as a method for studying the neural basis of differences in mathematical achievement. Hyper-connectivity of the intraparietal sulcus (IPS), a key locus of mathematical and numerical processing, has been associated with poor mathematical skills in childhood, whereas greater connectivity has been related to better performance in adulthood. No studies to date have considered its role in adolescence. Further, hippocampal connectivity can predict mathematical learning, yet no studies have considered its contributions to contemporaneous measures of math achievement. Here, we used seed-based resting-state fMRI analyses to examine IPS and hippocampal intrinsic functional connectivity relations to math achievement in a group of 31 adolescents (mean age=16.42 years, range 15-17), whose math performance spanned the 1% to 99% percentile. After controlling for IQ, IPS connectivity was negatively related to math achievement, akin to findings in children. However, the specific temporooccipital regions were more akin to the posterior loci implicated in adults. Hippocampal connectivity with frontal regions was also negatively correlated with concurrent math measures, which contrasts with results from learning studies. Finally, hyper-connectivity was not a global feature of low math performance, as math performance did not modulate connectivity of Heschl’s gyrus, a control seed not involved in math cognition. Our results provide preliminary evidence that adolescence is a transitional stage in which patterns found in childhood and adulthood can be observed; most notably, hyper-connectivity continues to be related to low math ability into this period.
CJEU clarifies that framing infringes copyright if TPMs are circumvented

Court of Justice of the European Union, VG Bild-Kunst v Stiftung Preußischer Kulturbesitz, C-392/19 EU:C:2021:181, 9 March 2021 The Court of Justice of the European Union (CJEU) has ruled that framing thumbnails in circumvention of effective technological protection measures (TPMs) is an act communication to the public. The dispute underlying the preliminary reference by the German Bundesgerichtshof arose between the Stiftung Preußischer Kulturbesitz (SPK), the operator of the German Digital Library (DBB) and collecting society VG Bild-Kunst (VBK). SPK sought to license the use of thumbnails as part of its digital offer, which VBK only agreed to under the condition that SPK implements effective technological protection measures (TPMs) that would prevent third parties from framing the thumbnails. SPK considered this condition to be unreasonable, while collecting societies, under the applicable German law, are obliged to grant licences on reasonable terms to any person requesting a...
Effective Attention Sheds Light On Interpretability

An attention matrix of a transformer self-attention sublayer can provably be decomposed into two components and only one of them (effective attention) contributes to the model output. This leads us to ask whether visualizing effective attention gives different conclusions than interpretation of standard attention. Using a subset of the GLUE tasks and BERT, we carry out an analysis to compare the two attention matrices, and show that their interpretations differ. Effective attention is less associated with the features related to the language modeling pretraining such as the separator token, and it has more potential to illustrate linguistic features captured by the model for solving the end-task. Given the found differences, we recommend using effective attention for studying a transformer's behavior since it is more pertinent to the model output by design.
Content matters: Measures of contextual diversity must consider semantic content

Measures of contextual diversity seek to replace word frequency by counting the number of contexts in which a word occurs rather than the raw number of occurrences (Adelman, Brown, &amp;amp; Quesada, 2006). It has repeatedly been shown that contextual diversity measures outperform word frequency on word recognition datasets (Adelman &amp;amp; Brown, 2008; Brysbaert &amp;amp; New, 2009). Recently, Hollis (2020) has questioned the importance of contextual diversity by demonstrating that when other variables of contextual occurrences are controlled for, diversity accounts for relatively small amounts of unique variance over word frequency. However, the analysis of Hollis (2020) did not take into account the semantic content of the contexts that words occur in. Johns, Dye, and Jones (2020) and Johns (2021) have recently shown that defining linguistic contexts at larger, and more ecologically valid, levels lead to contextual diversity measures that provide very large improvements over word frequency, especially when implemented with principles from the Semantic Distinctiveness Model of Jones, Johns, and Recchia (2012). Across a series of simulations, we demonstrate that the advantages of contextual diversity measures are dependent upon the usage of semantic representations of words to determine the uniqueness of contextual occurrences, where unique contextual occurrences provide a greater impact to a word’s lexical strength than redundant contextual occurrences.
Code2Image: Intelligent Code Analysis by Computer Vision Techniques and Application to Vulnerability Prediction

Intelligent code analysis has received increasing attention in parallel with the remarkable advances in the field of machine learning (ML) in recent years. A major challenge in leveraging ML for this purpose is to represent source code in a useful form that ML algorithms can accept as input. In this study, we present a novel method to represent source code as image while preserving semantic and syntactic properties, which paves the way for leveraging computer vision techniques to use for code analysis. Indeed the method makes it possible to directly enter the resulting image representation of source codes into deep learning (DL) algorithms as input without requiring any further data pre-processing or feature extraction step. We demonstrate feasibility and effectiveness of our method by realizing a vulnerability prediction use case over a public dataset containing a large number of real-world source code samples with performance evaluation in comparison to the state-of-art solutions. Our implementation is publicly available.
A Benchmarking on Cloud based Speech-To-Text Services for French Speech and Background Noise Effect

This study presents a large scale benchmarking on cloud based Speech-To-Text systems: {Google Cloud Speech-To-Text}, {Microsoft Azure Cognitive Services}, {Amazon Transcribe}, {IBM Watson Speech to Text}. For each systems, 40158 clean and noisy speech files about 101 hours are tested. Effect of background noise on STT quality is also evaluated with 5 different Signal-to-noise ratios from 40dB to 0dB. Results showed that {Microsoft Azure} provided lowest transcription error rate $9.09\%$ on clean speech, with high robustness to noisy environment. {Google Cloud} and {Amazon Transcribe} gave similar performance, but the latter is very limited for time-constraint usage. Though {IBM Watson} could work correctly in quiet conditions, it is highly sensible to noisy speech which could strongly limit its application in real life situations.
Application Research of ID3 Attribute Optimization Algorithm Based on Correlation Coefficient

As the traditional ID3 algorithm information entropy calculation method, it is prone to problems such as multi-value bias, large calculation amount and low efficiency. An improved ID3 algorithm is constructed based on Spearman's rank correlation coefficient, and the concept of conditional attribute correlation coefficient is introduced when calculating conditional attribute information gain, which optimizes the selection of conditional attributes, and can greatly simplify the construction of decision trees. Experimental data analysis shows that the accuracy of classification rules has also been greatly improved.
Simple Transparent Adversarial Examples

There has been a rise in the use of Machine Learning as a Service (MLaaS) Vision APIs as they offer multiple services including pre-built models and algorithms, which otherwise take a huge amount of resources if built from scratch. As these APIs get deployed for high-stakes applications, it's very important that they are robust to different manipulations. Recent works have only focused on typical adversarial attacks when evaluating the robustness of vision APIs. We propose two new aspects of adversarial image generation methods and evaluate them on the robustness of Google Cloud Vision API's optical character recognition service and object detection APIs deployed in real-world settings such as sightengine.com, picpurify.com, Google Cloud Vision API, and Microsoft Azure's Computer Vision API. Specifically, we go beyond the conventional small-noise adversarial attacks and introduce secret embedding and transparent adversarial examples as a simpler way to evaluate robustness. These methods are so straightforward that even non-specialists can craft such attacks. As a result, they pose a serious threat where APIs are used for high-stakes applications. Our transparent adversarial examples successfully evade state-of-the art object detections APIs such as Azure Cloud Vision (attack success rate 52%) and Google Cloud Vision (attack success rate 36%). 90% of the images have a secret embedded text that successfully fools the vision of time-limited humans but is detected by Google Cloud Vision API's optical character recognition. Complementing to current research, our results provide simple but unconventional methods on robustness evaluation.
A Study on the Informal Learning of English Majors based on English Learning App

Under the general trend of education reform and educational informationization, English teaching began to seek innovative development paths. At present, in the various media situations, English majors begin to rely more on modern technology, and informal learning has begun to rise in English learning. In informal learning, the learning model based on the English learning app is one of the main manifestations. This mobile learning-based English learning model is currently being sought after by students and has achieved good results. However, based on the requirements to further advance the learning style, relevant scholars need to study it more deeply to ensure a more scientific teaching mode. This paper focuses on the all-round exploration of the informal learning model based on the English learning APP.
SpeechMoE: Scaling to Large Acoustic Models with Dynamic Routing Mixture of Experts

Recently, Mixture of Experts (MoE) based Transformer has shown promising results in many domains. This is largely due to the following advantages of this architecture: firstly, MoE based Transformer can increase model capacity without computational cost increasing both at training and inference time. Besides, MoE based Transformer is a dynamic network which can adapt to the varying complexity of input instances in realworld applications. In this work, we explore the MoE based model for speech recognition, named SpeechMoE. To further control the sparsity of router activation and improve the diversity of gate values, we propose a sparsity L1 loss and a mean importance loss respectively. In addition, a new router architecture is used in SpeechMoE which can simultaneously utilize the information from a shared embedding network and the hierarchical representation of different MoE layers. Experimental results show that SpeechMoE can achieve lower character error rate (CER) with comparable computation cost than traditional static networks, providing 7.0%-23.0% relative CER improvements on four evaluation datasets.
Optimized Development of Web Front-end Development Technology

Abstract With the widespread use of the Internet in modern social life, the existing Web technology still has a large room for optimization and development in terms of loading time, response speed, and user Internet experience. Therefore, Internet technicians need to fully understand and master various Web front-end development technologies. Besides, through the technical optimization we can reduce the HTTP errors and requests. Moreover, in order to improve the user’s Internet experience we are supposed to control the file size, reduce the invalid responses and DNS queries reasonably.
A transform approach to polycyclic and serial codes over rings

In this paper, a transform approach is used for polycyclic and serial codes over finite local rings in the case that the defining polynomials have no multiple roots. This allows us to study them in terms of linear algebra and invariant subspaces as well as understand the duality in terms of the transform domain. We also make a characterization of when two polycyclic ambient spaces are Hamming-isometric.
Assessing the degree of ecological validity of your study: Introducing the Multidimensional Assessment of Research in Context (MARC) Tool

In cognitive neurosciences, fundamental principles of mental processes and functional brain organization have been established with highly controlled tasks and testing environments. Recent technical advances allowed the investigation of these functions and their brain mechanisms in naturalistic settings. The diversity in those approaches has been recently (Matusz et al. 2019a) classified via a three-category cycle including controlled laboratory, partially naturalistic laboratory, and naturalistic real-world research. Based on this model, we developed the Multidimensional Assessment of Research in Context (MARC) tool to easily delineate the approach researchers have taken in their study. MARC provides means to describe the degree of ecological validity for each component of a study (e.g. sample, location, stimuli, measures, etc.) and the study’s location on the cycle. The tool comprises seven questions concerning study’s characteristics. It outputs a summary of those and a compass plot, which can be used for presentations, pre-registration, grant proposals, and papers. It aims to improve drawing conclusions across studies, and raise awareness about generalizability of research findings.
Conditional Inference under Disjunctive Rationality

The question of conditional inference, i.e., of which conditional sentences of the form ``if A then, normally, B'' should follow from a set KB of such sentences, has been one of the classic questions of AI, with several well-known solutions proposed. Perhaps the most notable is the rational closure construction of Lehmann and Magidor, under which the set of inferred conditionals forms a rational consequence relation, i.e., satisfies all the rules of preferential reasoning, *plus* Rational Monotonicity. However, this last named rule is not universally accepted, and other researchers have advocated working within the larger class of *disjunctive* consequence relations, which satisfy the weaker requirement of Disjunctive Rationality. While there are convincing arguments that the rational closure forms the ``simplest'' rational consequence relation extending a given set of conditionals, the question of what is the simplest *disjunctive* consequence relation has not been explored. In this paper, we propose a solution to this question and explore some of its properties.
Upaya meningkatkan kemampuan membaca melalui media kartu gambar pada siswa kelas 1 SD Negeri Cikeusal Kidul 01 tahun pelajaran 2020/2021

Classroom action research has been carried out to improve and improve students' reading skills through classroom action research using the picture card method. This research was conducted in class I SD Negeri Cikeusal Kidul 01 for the 2020/2021 school year, with a total of 21 students. This study aims to determine whether learning using picture card media can improve students' reading skills. The focus of this study is to determine the level of students' reading skills, student learning outcomes, and teacher performance. The results showed an increase in student learning outcomes were 68.57 and 79.25, respectively. Student learning activities also increased, namely with an average score of 71.03 in the first cycle increasing to 73.91 in the second cycle. It can be concluded that the use of picture card media can improve students' reading skills.
From Virtual Smile Design to Guided Wax-up Using Adobe Photoshop

Aim:This technique aim to present a calculation-free method using Adobe Photoshop to apply the principles of facially driven smiles and to generate a wax-up based on measurements obtained from the final smile design.Background: The lack of quantifiable measurements and the subsequent unrealistic expectations that develop can limit the use of virtual smile design or augmented reality applications.Developing a standardized measurement method that guides the laboratory wax-up after virtual smile design may solve this problem and allow many clinicians to utilize it in their day-to-day practice.Technique: This technique article presents an efficient method of utilizing Adobe Photoshop software to create a facially driven smile design, to gather quantifiable measurements, and to fabricate a wax-up based on the digital design. Conclusion:This technique allows the restorative dentist to achieve more predictable esthetic results and provides the laboratory technician with quantifiable measurements that they can use to bring the vision of the dentist to life.
Students’ Views on the Use of WhatsApp during Covid-19 Pandemic: A Study at IAIN Batusangkar

This study aims to describe students’ views on the use of WhatsApp in English Teaching Department classes during the covid-19 pandemic. The impact of the Coronavirus disease 2019 (Covid-19) pandemic is now beginning to spread to the world of education. This condition makes the students and the lecturers used online media or online learning much longer than expected. In the online learning process, WhatsApp is one of the most used applications. This study used qualitative research. The research population was all 270 students of the English Teaching Department of IAIN Batusangkar. The sample involved 168 students by using stratified random sampling technique. Slovin’s formula was used to measure the sample. The research instrument was questionnaires. It was designed in the type of closed-ended and open-ended forms. The questionnaire consisted of 21 statements. Then, to analyze the data from a closed-ended questionnaire, the researchers used Guttman Scale. Meanwhile, to analyze the open-ended questionnaire, the researchers classified students’ responses and then described the results. The finding of the study indicated that students’ views on the use of WhatsApp (WA) in English Teaching Department classes during the covid-19 pandemic mostly positive (73.2%) and the negative responses only (26.8%). Furthermore, students’ views on the use of WA includes WA as media in doing online learning process; WA as an educational media; WA as an evaluation and assessment benchmarks; WA as an application of sharing information; WA as the application to do group discussion; and WA as media to increase literation and to share learning materials. This research implies that WA could be used to do the online learning process because it empirically helps the students to get enthusiastically involved in learning activities.
From Tax Compliance in Natural Language to Executable Calculations: Combining Lexical-grammar-based Parsing and Machine Learning

&#x0D; &#x0D; &#x0D; Regulatory agencies publish tax-compliance content written in natural language intended for human consumption. There has been very little work on automated methods for interpreting this content and for generating executable calculations from it. In this paper, we describe a combination of lexical grammar-based parsing with encoder-decoder architectures for automatically bootstrapping executable calculations from natural language. The combination is particularly suitable for domains such as compliance where training data is scarce and accuracy of interpretation is of high importance. We provide an overview of the implementation for North American income-tax forms. &#x0D; &#x0D; &#x0D;