Reviewerly/exHarmony huggingface dataset:
Improving Transfer Learning in Unsupervised Language Adaptation

Unsupervised language adaptation aims to improve the cross-lingual ability of models that are fine-tuned on a specific task and source language, without requiring labeled data on the target language. On the other hand, recent multilingual language models (such as mBERT) have achieved new state-of-the-art results on a variety of tasks and languages, when employed in a direct transfer approach. In this work, we explore recently proposed unsupervised language adaptation methods – Adversarial Training and Encoder Alignment – to fine-tune language models on a specific task and language pairs, showing that the cross-lingual ability of the models can be further improved. We focus on two conceptually different tasks, Natural Language Inference and Sentiment Analysis, and analyze the performance of the explored models. In particular, Encoder Alignment is the best approach for most of the settings explored in this work, only underperforming in the presence of domain-shift between source and target languages.
Graph Fraud Detection Based on Accessibility Score Distributions

Graph fraud detection approaches traditionally present frauds as subgraphs and focus on characteristics of the fraudulent subgraphs: unexpectedly high densities or sparse connections with the rest of the graph. However, frauds can easily circumvent such approaches by manipulating their subgraph density or making connections to honest user groups. We focus on a trait that is hard for fraudsters to manipulate: the unidirectionality of communication between honest users and fraudsters. We define an accessibility score to quantify the unidirectionality, then prove the unidirectionality induces skewed accessibility score distributions for fraudsters. We propose SkewA, a novel fraud detection method that measures the skewness in accessibility score distributions and uses it as an honesty metric. SkewA is (a) robust to frauds with low density and various types of camouflages, (b) theoretically sound: we analyze how the unidirectionality brings skewed accessibility score distributions, and (c) effective: showing up to 95.6% accuracy in real-world data where all competitors fail to detect any fraud.
Bayesian Testing of Linear Versus Nonlinear Effects Using Gaussian Process Priors

A Bayes factor is proposed for testing whether the effect of a key predictor variable on a dependent variable is linear or nonlinear, possibly while controlling for certain covariates. The test can be used (i) in substantive research for assessing the nature of the relationship between certain variables based on scientific expectations, and (ii) for statistical model building to infer whether a (transformed) variable should be added as a linear or nonlinear predictor in a regression model. Under the nonlinear model, a Gaussian process prior is employed using a parameterization similar to Zellner’s g prior resulting in a scale-invariant test. Unlike existing p-values, the proposed Bayes factor can be used for quantifying the relative evidence in the data in favor of linearity. Furthermore the Bayes factor does not overestimate the evidence against the linear null model resulting in more parsimonious models. An extension is proposed for Bayesian one-sided testing of whether a nonlinear effect is consistently positive, consistently negative, or neither. Applications are provided from various fields including social network research and education.
Leveraging Adversarial Training to Facilitate Grammatical Error Correction

Grammatical error correction (GEC) task aims to detect and correct grammatical errors in sentences. Recently, the pre-trained language model has provided a strong baseline for GEC and achieved excellent results by fine-tuning on a small amount of annotated data. However, due to the lack of large-scale erroneous-corrected parallel datasets, these models tend to suffer from the problem of overfitting. Previous researchers have proposed a variety of data augmentation methods to generate more training data and enlarge the dataset, but these methods either rely on rules to generate grammatical errors and are not automated, or produce errors that do not match human writing errors. The pre-trained model only improves significantly after task-specific data fine-tuning; otherwise, the highly noisy data can impair the performance of the pre-trained model. To address this issue, we propose a method to enhance the robustness of the model based on adversarial training. This approach constructs the adversarial samples and treats them as the augmented data. Unlike previous methods that introduce token-level noise, our method introduces embedding-level noise and can obtain extra samples that are close to human writing errors. Besides, we employ the adversarial consistency constraint to reduce the gap between the adversarial sample and the original sample. The experimental results demonstrate that our method can further boost the performance of the pre-trained model on GEC task.
Tangled, tangy, microbial threads: Textural methods for rendering past, present, and future sensory memories

How to render sensory memory? In this article, I speculate on the possibilities of textural methods which attend closely to textile forms, specifically embroidery, as a way to explore this enduring question in multimodal research. To open up concerns about bodily relations between humans, as well as the more-than-human bodies we share worlds with, this article focuses on sensory memory fragments of encounters with the microbial conglomerations of sourdough bread starter. I offer three bubbling, sour-sweet texts: 1) an archived auto-ethnographic account of learning how to make a sourdough starter; 2) a social-media inspired piece on the sticky home archives of quarantine; and 3) a future speculative citizen science project. These fragments co-exist with microbes I have embroidered on ancient linens. From the tangy strings of sourdough histories, and the tangled threads in cloth I draw concrete methodological suggestions for new directions in textural research projects, such as material fieldnotes and crafted data. In doing so, I join other authors in this special issue in the call for multimodal forms of ethnographic storytelling about sensory memory, in this case one that attends not only to messy entanglements with bodies but also their textural, material, layered histories extending into the depth of their surfaces.
A Study on the Condition and Direction for the Improvement of the Curriculum of General Education －Focused on the Case of I University

The objective of this study is to explore the conditions and direction that should be closely reviewed in the process of reorganizing the curriculum for general education. In particular, this study concentrated on the establishment of a detailed execution plan and the macroscopic direction of general education. It did so by exploring the practical logic and theoretical perspective for reorganizing the curriculum for general education when the accountability of general education is to be emphasized. For this, this study explored the theoretical background for the reorganization of the curriculum focusing on the foundation, structure, and contents of the curriculum for general education. It also diagnosed some domestic and foreign cases of reorganizing the curriculum for general education, and the curriculum for general education of I university in regards to its foundation, structure, and contents. It then secured the argument for the validity of the reorganization of the curriculum for general education of I university focusing on the results drawn through both the theoretical and case research. This study established the goal and objectives found in the general education of I university regarding the foundation of its curriculum for general education, and attempted the reorganization of basic and core general education when it came to its structure and its contents. In conclusion, this study presented the professors’ responsible educational activities, the administrative support for the qualitative improvement of general education, and the establishment of the evaluation system of the curriculum for general education. Having done so, we have pointed out the measures we need to take in order to offer a more stable and settled general education curriculum.
Minerva Surgical, Inc. v. Hologic, Inc.

Biotechnology Law ReportVol. 40, No. 5 Original ArticlesMinerva Surgical, Inc. v. Hologic, Inc.Supreme Court of the United States, 2021141 S.Ct. 2298Edited for Biotechnology Law Report by Christopher M. HolmanEdited for Biotechnology Law Report by Christopher M. HolmanChristopher M. Holman is a Professor of Law at the University of Missouri–Kansas City School of Law; a Senior Scholar at the Center for the Protection of Intellectual Property at the Antonin Scalia Law School, George Mason University; and the Executive Editor of Biotechnology Law Report.*The synopsis is original content and the decision itself has been condensed for ease of readability.Search for more papers by this authorPublished Online:12 Oct 2021https://doi.org/10.1089/blr.2021.29247.cmhAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 40Issue 5Oct 2021 InformationCopyright 2021, Mary Ann Liebert, Inc., publishersTo cite this article:Edited for Biotechnology Law Report by Christopher M. Holman.Minerva Surgical, Inc. v. Hologic, Inc..Biotechnology Law Report.Oct 2021.360-367.http://doi.org/10.1089/blr.2021.29247.cmhPublished in Volume: 40 Issue 5: October 12, 2021Online Ahead of Print:September 14, 2021PDF download
Problems of protecting human rights in the context of the COVID-19 pandemic

As part of the global response of States to the COVID-19 pandemic, most governments are currently forced to take emergency measures to respond to the emerging national threat, introducing bans and restrictions, establishing additional responsibilities and measures to control citizens, which inevitably raises the question of the need to preserve the fundamental principles of the rule of law and the basic constitutional rights and freedoms of man and citizen in the context of the pandemic. The purpose of this article is to analyze the main problems of protecting human and civil rights in the context of a pandemic and to determine the basic principles and rules on which States are based when adopting legislation that restricts the rights and freedoms of citizens.
Determination of the Most Relevant Features to Improve the Performance of RF Classifier in Human Activity Recognition

The impact that neurodegenerative diseases have in our society, have made human activity recognition (HAR) arise as a relevant field of study. The quality of life of people with such conditions, can be significantly improved with the outcomes of the projects within this area. The application of machine learning techniques on data from low level sensors such as accelerometers is the base of HAR. To improve the performance of these classifiers, it is necessary to carry out an adequate training process. To improve the training process, an analysis of the different features used in literature to tackle these problems was performed on datasets constructed with students performing 18 different activities of daily living. The outcome of the process shows that an adequate selection of features improves the performance of Random Forest from 94.6% to 97.2%. It was also found that 78 features explain 80% of the variability.
SW-LZMA: Parallel Implementation of LZMA Based on SW26010 Many-Core Processor

With the development of high-performance computing and big data applications, the scale of data transmitted, stored, and processed by high-performance computing cluster systems is increasing explosively. Efficient compression of large-scale data and reducing the space required for data storage and transmission is one of the keys to improving the performance of high-performance computing cluster systems. In this paper, we present SW-LZMA, a parallel design and optimization of LZMA based on the Sunway 26010 heterogeneous many-core processor. Combined with the characteristics of SW26010 processors, we analyse the storage space requirements, memory access characteristics, and hotspot functions of the LZMA algorithm and implement the thread-level parallelism of the LZMA algorithm based on Athread interface. Furthermore, we make a fine-grained layout of LDM address space to achieve DMA double buffer cyclic sliding window algorithm, which optimizes the performance of SW-LZMA. The experimental results show that compared with the serial baseline implementation of LZMA, the parallel LZMA algorithm obtains a maximum speedup ratio of 4.1 times using the Silesia corpus benchmark, while on the large-scale data set, speedup is 5.3 times.
Optimised Code-Switched Language Model Data Augmentation in Four Under-Resourced South African Languages

Code-switching in South African languages is common but data for language modelling remains extremely scarce. We present techniques that allow recurrent neural networks (LSTMs) to be better applied as generative models to the task of producing artificial code-switched text that can be used to augment the small training sets. We propose the application of prompting to favour the generation of sentences with intra-sentential language switches, and introduce an extensive LSTM hyperparameter search that specifically optimises the utility of the artificially generated code-switched text. We use these strategies to generate artificial code-switched text for four under-resourced South African languages and evaluate the utility of this additional data for language modelling. We find that the optimised models are able to generate text that leads to consistent perplexity and word error rate improvements for all four language pairs, especially at language switches. This is an improvement on previous work using the same speech data in which text generated without such optimisation did not provide improved performance. We conclude that prompting and targeted hyperparameter optimisation are an effective means of improving language model data augmentation for code-switched speech recognition.
Semantic analysis of news of self-inflicted deaths in Yucatán

Yucatán is one of the states in Mexico with high suicide rates for several years compared to the rest of the country. Because of this, local newspapers have emphasized publishing-related news. Many of them contain associated expressions that help characterize the text and define its semantics. This work presents the implementation of a text classification algorithm based on the semantic analysis of suicide news in Yucatán. Different classification models were built and evaluated, and the best results were obtained with Logistic Regression using TF-IDF. The algorithm was tested with 122 news items, of which it managed to identify 116 news items correctly, with effectiveness of 95.08%.
DevOps Job Roles: A Multivocal Literature Review

DevOps bridges the gap between software development and operations to provide rapid deliveries and integrated collaboration. However, DevOps entails lots of factors and challenges involved in its implementation including technical, organizational and personnel aspects. Focusing on the last set of aspects, this paper identifies current DevOps job roles, an aspect that is crucial to implement and to support DevOps practices in organizations. It also highlights collaboration between different actors under different automation levels in DevOps to deliver software at high speed.
Scouting from the Perspective of Increasing the Sense of Security

Scouting is a system that developed at the beginning of the last century and is now entering the era of information society.A sense of brotherhood increases the feeling of security.Scouting is a proposal for an upbringing based on active and compassionate values related to relationships with others, for others and for self-improvement and the common pursuit of the ideal.Scout activity, his willingness to serve, experience and interact with other brothers scouts around their world promotes the opening of values in their different dimensions.
Research on the Establishment of Standard System Based on Knowledge Graph

The standard system has become one of the important aspects in the modernization of social governance system and governance ability. The establishment of the standard system should not only face the top-level design, but also for the specific implementation. In the process of establishing the standard system by traditional methods, with the help of experts' experience, the future development trend and key development fields are predicted, and the standard system is planned by “brainstorming”. With the continuous progress of information technology, as the “universal language”, standard connects the world by its typical network attributes. With the rapid developed of knowledge graph (KG) technology, establishing the relationship between different standards, and analyzing in depth, we can get the general framework of the standard system. At the same time, combined with the experience of experts and the future development trend of technology, a relatively complete standard system is formed. The results show that the method adopted in this paper has certain guiding significance for the establishment of a scientific standard system.
Automatic Detection of LST-Type Polyp by CNN Using Depth Map

Lateral Spreading Tumor (LST) type flat polyps are sometimes overlooked and difficult to be detected among many lesions. This paper proposes a CNN model of multiple input and multiple output structure to detect LST-type polyp with high accuracy, which is based on U-Net architecture for the segmentation. Not only the original endoscope image but also depth map is also used to the original CNN structure of 2 inputs and 4 outputs. Here, proposed method obtains 3D shape from the original endoscope image and creates the depth map under the condition of point light source illumination and perspective projection. Higher accuracy of 85% was obtained for the detection of LST-type polyp by the proposed method. It is shown that the multiple input-output structure of U-Net model gives the higher performance of segmentation problem using both of original endoscope image and depth map.
Secure storage of Multi-level Classified Computer Data Based on the Combination of Differential Evolution Algorithm

Storage security of multi-level classified data is a vital core link in the construction of a national information system. Given the current problems of relatively long time consumption for storage, high CPU occupancy, high congestion rate, and poor anti-attack performance in the application of Hadoop technology to store multi-level classified data, a secure storage system for multi-level classified data based on the combination of differential evolution algorithm is proposed and designed in this paper. The secure storage system for multi-level classified data, including a client, a relay control server, and a storage server is preliminarily designed. The security performance of the multi-level classified data such as integrity, confidentiality, authentication mechanism, and access control strategy, are analyzed in detail. The system performance test results suggest that the system designed can implement secure and rapid storage of multi-level classified data while reducing the CPU occupancy and system congestion rates at the same time.
Transformation of professional competencies of a modern specialist: the formation and development of soft skills

The article is devoted to the study of the issues related to the change in the educational trajectory of a modern specialist due to the need to form new professional and personal competencies. The author has studied and presented new flexible and professional skills that have become in demand in the conditions of the technological revolution and the dynamically changing business environment of the beginning of the 21st century. The author emphasizes the importance of the formation and development of manager soft skills in entrepreneurial and managerial activities.
Genuinely entangling uncorrelated atoms via Jaynes–Cummings interactions

We study three independent pairs of Jaynes–Cummings systems such that two atoms might be correlated with each other but the third atom is uncorrelated with rest. We investigate the conditions under which these uncorrelated three atoms may become genuinely entangled. We find that this task is impossible if the cavity interacting with uncorrelated atom share classical correlations with any other cavity. We observe that atomic state can become genuine multipartite entangled, at least if the cavity with uncorrelated atom, is highly entangled with any other cavity. This is an interesting and non-trivial observation and may serve as another technique to generate multipartite entangled atoms via JC-interactions.
Change detection method for intensity VHF wavelength-resolution SAR images

This paper presents the proposal of a new change detection method for intensity VHF wavelength-resolution images. High-amplitude pixels are related to the presence of strong scatterers, resulting in high detection probability performance. However, the number of false alarms tends to be high too. In this initial study, difference images are considered to reduce the influence of the strong scatterers that are not related to targets, i.e., present in both surveillance and reference images. The proposed change detection method is based on a likelihood-ratio test, where the tested hypothesis is the bivariate exponential distribution. The derivation of the proposed likelihood test is presented. Finally, the proposed change detection method is assessed considering data measured with the CARABAS II VHF UWB SAR system. Preliminary results show that the proposed method is efficient in detecting positive changes.
Dynamic Kolmogorov Approach to RNN Universality

Feed-Forward Neural Networks are known to be universal approximators, meaning that they can approach any measurable function at any given level of precision, provided that their hidden layer is large enough. However, such property does not ensure practical convergence via training, and certain types of problems have been found to be best solved via other neural structures. Time-series analysis, for example, has been frequently associated with Recurrent Neural Networks or more recently LSTM architectures. However, are those endowed with the same universality as FFNN? Given that RNN universality has already been proven on measurable dynamic systems, this paper shows that any multivariate continuous function can be written as a dynamic system uploaded once per variable, thanks to the Kolmogorov Representation Theorem, hence concluding to the continuous universality of RNN architectures.
Vladimir Mironov’s metaphysics and university education transformation

The article deals with the specificity of philosophical knowledge, metaphysics as its semantic core and the problems of transforming modern humanitarian university education, which was developed in the work of Vladimir Mironov. V. V. Mironov is a well-known Russian scientist, doctor of philosophy, professor, corresponding member of the Russian Academy of Sciences, dean of the faculty of philosophy of the Lomonosov Moscow State University. The author analyses current problems related to the role of philosophy in shaping the image of the future in modern culture, reforming university education, the consequences of introducing scientometric methods into the educational process, the readiness of higher education responding to the challenges of the times, global instability, commercialisation, digitalisation, etc. The scientific position of V. V. Mironov on topical problems of transforming modern society, its assessment and reflections on the prospects for teaching philosophy and the humanities.