Reviewerly/exHarmony huggingface dataset:
Verb verb complex predicates in Old and Middle Japanese

This chapter describes the main features of Verb Verb complex predicates in Old and Middle Japanese (8th to 16th centuries) and discusses changes that took place between Old Japanese and Modern Japanese. Old Japanese had complex verbal predicate constructions which resemble the Modern Japanese Types 1–3, and in addition a V1 aktionsart verb construction, which is not found in later stages and which is hypothesized to have played a crucial role in the development of complex predicates in Japanese. The wordhood of complex verbal predicates is addressed. Verb Verb complex predicates did not constitute tight morphological units in Old and Early Middle Japanese, but developed into morphological words in Late Middle and Modern Japanese. It is proposed that the loss of subordinating function of the infinitive played an important part in this change. It is also observed that the Modern Japanese Type 4 (V<italic>te</italic> V) is a Late Middle Japanese innovation which is not a prototypical complex predicate verb construction but rather an auxiliary verb construction.
Multi-channel CNN-BiGRU Neural Network For Sentiment Analysis

Sentiment analysis is generally used in the industry for recognizing the thoughts of people on product, company, or organization. For that, widely available textual data such as user reviews, comments, and tweets are used. Negation words are an essential part of any textual data. Negation words reverse or negate the polarity or meaning of the sentence. In this paper, we concentrate on binary sentiment classification with attention to the negation words. We propose a novel model for sentiment analysis that uses multi-channel neural networks. This model uses morphological data and negation word embeddings as parallel input. We test this model on various datasets available, and our experimental results are promising.
Demystify Optimization Challenges in Multilingual Transformers

Multilingual Transformer improves parameter efficiency and crosslingual transfer. How to effectively train multilingual models has not been well studied. Using multilingual machine translation as a testbed, we study optimization challenges from loss landscape and parameter plasticity perspectives. We found that imbalanced training data poses task interference between high and low resource languages, characterized by nearly orthogonal gradients for major parameters and the optimization trajectory being mostly dominated by high resource. We show that local curvature of the loss surface affects the degree of interference, and existing heuristics of data subsampling implicitly reduces the sharpness, although still face a trade-off between high and low resource languages. We propose a principled multi-objective optimization algorithm, Curvature Aware Task Scaling (CATS), which improves both optimization and generalization especially for low resource. Experiments on TED, WMT and OPUS-100 benchmarks demonstrate that CATS advances the Pareto front of accuracy while being efficient to apply to massive multilingual settings at the scale of 100 languages.
MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models

Retrieval question answering (ReQA) is the task of retrieving a sentence-level answer to a question from an open corpus (Ahmad et al.,2019).This dataset paper presents MultiReQA, a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets. We explore systematic retrieval based evaluation and transfer learning across domains over these datasets using a number of strong base-lines including two supervised neural models, based on fine-tuning BERT and USE-QA models respectively, as well as a surprisingly effective information retrieval baseline, BM25. Five of these tasks contain both training and test data, while three contain test data only. Performing cross training on the five tasks with training data shows that while a general model covering all domains is achievable, the best performance is often obtained by training exclusively on in-domain data.
ACVIZ: A tool for the visual analysis of the configuration of algorithms with irace

This paper introduces acviz, a tool that helps to analyze the automatic configuration of algorithms with irace. It provides a visual representation of the configuration process, allowing users to extract useful information, e.g. how the configurations evolve over time. When test data is available, acviz also shows the performance of each configuration on the test instances. Using this visualization, users can analyze and compare the quality of the resulting configurations and observe the performance differences on training and test instances.
Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models.

Synthetic data generation is widely known to boost the accuracy of neural grammatical error correction (GEC) systems, but existing methods often lack diversity or are too simplistic to generate the broad range of grammatical errors made by human writers. In this work, we use error type tags from automatic annotation tools such as ERRANT to guide synthetic data generation. We compare several models that can produce an ungrammatical sentence given a clean sentence and an error type tag. We use these models to build a new, large synthetic pre-training data set with error tag frequency distributions matching a given development set. Our synthetic data set yields large and consistent gains, improving the state-of-the-art on the BEA-19 and CoNLL-14 test sets. We also show that our approach is particularly effective in adapting a GEC system, trained on mixed native and non-native English, to a native English test set, even surpassing real training data consisting of high-quality sentence pairs.
Wine Ontology Influence in a Recommendation System

Wine is the second most popular alcoholic drink in the world behind beer. With the rise of e-commerce, recommendation systems have become a very important factor in the success of business. Recommendation systems analyze metadata to predict if, for example, a user will recommend a product. The metadata consist mostly of former reviews or web traffic from the same user. For this reason, we investigate what would happen if the information analyzed by a recommendation system was insufficient. In this paper, we explore the effects of a new wine ontology in a recommendation system. We created our own wine ontology and then made two sets of tests for each dataset. In both sets of tests, we applied four machine learning clustering algorithms that had the objective of predicting if a user recommends a wine product. The only difference between each set of tests is the attributes contained in the dataset. In the first set of tests, the datasets were influenced by the ontology, and in the second set, the only information about a wine product is its name. We compared the two test sets’ results and observed that there was a significant increase in classification accuracy when using a dataset with the proposed ontology. We demonstrate the general applicability of the methodology to other cases, applying our proposal to an Amazon product review dataset.
Rapid Medical Images Restoration Combining Parametric Wiener Filtering and Wave Atom Transform Based on Local Adaptive Shrinkage

Supervised image restoration is a process of reconstructing or recovering an image that has been degraded by using a priori knowledge of the degradation phenomenon. This includes deblurring images degraded by the limitations of sensors or source of captures, in addition to noise filtering and correction of geometric distortion due to sensors. Generally, restoring original images from their distorted form is a necessary step in many domains such as medical domain. In the literature, there are several classical medical images restoration techniques such as Wiener filtering. Thus, restoration techniques are oriented toward modeling the degradation and applying the inverse process in order to find an estimate of the original image. To recover the original medical image, Wiener filter needs a prior knowledge of the degradation phenomenon caused by the imaging system, the blurred image, and the statistical properties of the noise process. In this work, we propose a new fast algorithm for supervised medical images restoration that does not require a priori knowledge about the noise distribution. The degraded image is first deconvoluted in Fourier space by parametric Wiener filtering, then it is smoothed by the wave atom transform after applying a local adaptive shrinkage to its coefficients. Experiment results are very interesting and show the efficiency of the suggested method based on a comparison study.
hBert + BiasCorp - Fighting Racism on the Web

Subtle and overt racism is still present both in physical and online communities today and has impacted many lives in different segments of the society. In this short piece of work, we present how we’re tackling this societal issue with Natural Language Processing. We are releasing BiasCorp, a dataset containing 139,090 comments and news segment from three specific sources - Fox News, BreitbartNews and YouTube. The first batch (45,000 manually annotated) is ready for publication. We are currently in the final phase of manually labeling the remaining dataset using Amazon Mechanical Turk. BERT has been used widely in several downstream tasks. In this work, we present hBERT, where we modify certain layers of the pretrained BERT model with the new Hopfield Layer. hBert generalizes well across different distributions with the added advantage of a reduced model complexity. We are also releasing a JavaScript library 3 and a Chrome Extension Application, to help developers make use of our trained model in web applications (say chat application) and for users to identify and report racially biased contents on the web respectively
BERT meets Shapley: Extending SHAP Explanations to Transformer-based Classifiers

Transformer-based neural networks offer very good classification performance across a wide range of domains, but do not provide explanations of their predictions. While several explanation methods, including SHAP, address the problem of interpreting deep learning models, they are not adapted to operate on state-of-the-art transformer-based neural networks such as BERT. Another shortcoming of these methods is that their visualization of explanations in the form of lists of most relevant words does not take into account the sequential and structurally dependent nature of text. This paper proposes the TransSHAP method that adapts SHAP to transformer models including BERT-based text classifiers. It advances SHAP visualizations by showing explanations in a sequential manner, assessed by human evaluators as competitive to state-of-the-art solutions.
En-Is Synthetic Parallel Corpus

Synthetic back-translated training corpus for neural machine translation. The GreynirT2T Transformer network created the corpus by translating Icelandic and English sentences. The English sentences (44,7m) are retrieved from the Wikipedia, Newscrawl and Europarl corpora. The Icelandic sentences (31,3m) are sourced from the Icelandic Gigaword Corpus.Samhliða gervimalheild með bakþýddum þjalfunargognum fyrir velþýðingar. Tauganetið GreynirT2T Transformer bjo til malheildina með þvi að þýða enskar og islenskar setningar. Ensku setningarnar (44,7m) eru fengnar ur Wikipedia, Newscrawl og Europarl malheildunum. Þaer islensku eru fengnar ur Risamalheildinni (31,3m).
Ensuring a safe and equitable workspace: The importance and feasibility of a Code of Conduct, along with clear policies regarding authorship and team membership

We discuss how a Code of Conduct-type policy can foster a safer and more equitable environment within mission teams and research groups, as well as professional societies and conferences/meetings.Such a policy is highly complementary to other common Rules of the Road-type policies for NASA and NSF-funded science endeavors, and can be instrumental in creating a more inclusive culture.Eight recommendations are provided to increase effectiveness of Code of Conducttype
An Expert System for Laptop Fault Diagnostic Assistance

Laptop has always been a very useful tool for studies and most university students own at least one laptop. However, not all university student knows how to diagnose and troubleshoot their laptop when it is not working right. Most university student would rather hire computer technician to fix their laptop even though the solution to their laptop problem is rather simple. As cost of living increases every year, so does the cost of hiring a computer technician. Therefore, an expert system for laptop fault diagnostics was proposed to be developed. The objective of this study is to develop an expert system capable of diagnosing the cause of a laptop problem by analyzing user’s answers to the questions displayed by the system. The proposed system would also provide easy to follow solution to the user on how to solve the problem. The methodological approach to developing this system is by following waterfall model. There are five phases in a waterfall model, however only four phases will be executed namely requirement, design, implementation, testing phase. By using this system, user can easily identify possible root causes to their laptop problem by answering few questions related to their laptop problem. On top of that, the expert system will provide appropriate recommendation to the diagnosed laptop problem. Using this system, the time required to diagnose a computer as compared to manual diagnosis will reduce drastically. Most process in manual diagnosis will be automated and handled by the expert system. Lastly, user can save up unnecessary expenditure used to hire a computer technician.
Virtual Machine Consolidation for Stochastic Load Balancing in Cloud Data Center Management

Cloud computing is able to managing a massive quantity of growing work for the use of enterprise clients in a specified way Virtualization, which makes assumptions the network resources and makes it simple to control, is an important enabling technology for cloud computing. Computing is being used in the proposed work to distribute cloud services tailored to the needs and to promote the smart grid principle. “Skewness” concept was delivered here wherein equal was reducing to combine workloads to enhance the usage of the server. The complexities of on-demand allocation of resources arise from managing customer demands. As a result, the use of vms technologies has proved to be helpful in terms of resource provisioning. The use of virtualized environments is expected to reduce primarily consist connection speed while also executing tasks in accordance with cloud resource availability. This implementation can be use local negotiation based VM consolidation mechanism to predict each job request and reduce overloads to create virtual space at the time of multiple requests. The proposed system implement co-location approach to combine unused small spaces to create new virtual space for improves the performance of server. Also implement self-destruction approach to eliminate the invalid data based on time to live property. The proposed framework is executed in genuine time with effective asset allotment. In this system to begin with broaden a forecast show which will gauge the parcel sizes of decrease commitments at runtime. And it can detect information skewness in real time and allocate extra asses for mordant of large walls that help us complete faster.
TRUNAJOD: A text complexity library to enhance natural language processing

We present TRUNAJOD, a text complexity analysis tool that includes a wide variety of linguistics measurements that can be extracted from texts as an approximation for readability, coherence, and cohesion.The features that TRUNAJOD can extract from the text are based on the literature and can be separated into the following categories: discourse markers, emotions, entity gridbased measurements, givenness, lexical-semantic norms, semantic measures, surface proxies, etc.In this first version of TRUNAJOD, we mainly support the Spanish language, but several features support any language that has proper natural language processing POS tagging and dependency parsing capabilities.Finally, we show how TRUNAJOD could be used in applied research.
Desarrollo de una aplicación en el robot NAO H25 para la enseñanza y comprobación de la operación multiplicación con el método Maya

This research document shows the development of an application implemented in the humanoid robot NAO H25, using the methodology of Analysis, Design, Development, Implementation and Evaluation (ADDIE). Currently, in most public institutions of basic education, there is no robotic technology that the teacher uses for teaching a particular subject. The purpose of the application is that it works as a didactic resource, with which teachers and students of basic education can use as a form of support for the teaching of the multiplication operation. This application is capable of making the NAO H25 robot explain the multiplication operation in an arithmetic way and carry out its verification by means of the Mayan method. When the application is implemented in the NAO H25 robot, it acts as a trainer in the topic of multiplications, following a didactic sequence as a form of support for the development of the explanation. For the moment, tests were carried out on a group of 32 students of the computer engineering degree at this university centre. Where it was found that 94% were interested in the robot acting as a capacitor. 97% would like more applications of this type to be made for learning other subjects. The development of the application is with the motive of obtaining a significant learning in the students of third year of basic education, fomenting the interest by the technology.
Privacy-Preserved Blockchain-Federated-Learning for Medical Image Analysis Towards Multiple Parties

To share the patient\textquoteright s data in the blockchain network can help to learn the accurate deep learning model for the better prediction of COVID-19 patients. However, privacy (e.g., data leakage) and security (e.g., reliability or trust of data) concerns are the main challenging task for the health care centers. To solve this challenging task, this article designs a privacy-preserving framework based on federated learning and blockchain. In the first step, we train the local model by using the capsule network for the segmentation and classification of the COVID-19 images. The segmentation aims to extract nodules and classification to train the model. In the second step, we secure the local model through the homomorphic encryption scheme. The designed scheme encrypts and decrypts the gradients for federated learning. Moreover, for the decentralization of the model, we design a blockchain-based federated learning algorithm that can aggregate the gradients and update the local model. In this way, the proposed encryption scheme achieves the data provider privacy, and blockchain guarantees the reliability of the shared data. The experiment results demonstrate the performance of the proposed scheme.
Performance Analysis of Different Filters for Digital Image Processing

Digital image processing is one of the drastically growing areas used in various real- time industries like medical, satellite, remote sensing, and pattern recognition. The output of the image processing depends on the quality of the image. Filters are used to modify the images, such as removing the noise and smoothing the images. It is essential to suppress the high- frequency values in the image for smoothening and improving the low-frequency values to enhance the image of strengthening else it doesn't provide good output. This paper discussed various filters and their functionalities concerning digital image processing. Here linear, as well as non-linear filters, are presented. It is easy to decide about the better filter for improving the image processing output from the discussion.
Digital educational environment: training future teachers to design additional education

Abstract The concepts of further education - care and development are considered. Additional general educational programs are justified taking into account age and individual characteristics of children, adolescents and youth. The possibilities of a digital educational environment for teaching future teachers to design additional chemical education in primary and secondary schools are considered.
Deep Learning based Privacy Information Identification approach for Unstructured Text

Abstract Data sharing sometimes brings the privacy disclosure risk. Anonymization methods such as k-anonymity, l-diversity prevent privacy disclosure, but such methods are suitable for structured text. There are a lot of unstructured texts in people’s lives (such as social network texts, clinical texts), and identifying and structuring the private information(PI) of unstructured texts is a problem. Based on this, we propose a deep learning-based unstructured text PI identification approach, which can extract PI in unstructured text, associate the PI with the corresponding subject, and organize it into structured data, to support follow-up anonymization. This approach is divided into two tasks: PI identification and PI association. we respectively propose a sequence labeling model based on the RoBERTa-BiLSTM-CRF hybrid neural network and a PI association method based on the RoBERTa-HCR hybrid neural network to identify PI and organize it into structured data. The experimental results show that, compared with the benchmark model, RoBEERTa-BiLSTM-CRF has better performance; compared with the current Chinese coreference resolution model, the average F1-score value of RoBERTa-HCR is increased by 6%.
TiBroco: A Fast and Secure Distributed Learning Framework for Tiered Wireless Edge Networks

Recent proliferation of mobile devices and edge servers (e.g., small base stations) strongly motivates distributed learning at the wireless edge. In this paper, we propose a fast and secure distributed learning framework that utilizes computing resources at edge servers as well as distributed computing devices in tiered wireless edge networks. A fundamental lower bound is derived on the computational load that perfectly tolerates Byzantine attacks at both tiers. TiBroco, a hierarchical coding framework achieving this theoretically minimum computational load is proposed, which guarantees secure distributed learning by combating Byzantines. A fast distributed learning is possible by precisely allocating loads to the computing devices and edge servers, and also utilizing the broadcast nature of wireless devices. Extensive experimental results on Amazon EC2 indicate that our TiBroco allows significantly faster distributed learning than existing methods while guaranteeing full tolerance against Byzantine attacks at both tiers.