Reviewerly/exHarmony huggingface dataset:
Image Contrast Optimization using Local Color Correction and Fuzzy Intensification

Global image enhancement techniques are used to enhance contrast in images but these techniques are found to be under-enhanced or over-enhanced in differently illuminated regions of the image.Local color correction methods work on local pixel regions to optimize the color contrast enhancement but they also have been found to show a lag while covering pixel regions which are overexposed, compared to those which are underexposed causing local artifacts.In this work, we overcome the shortcomings of both the local color correction and global color correction.This method uses local color correction in the Hue Saturation Luminance (HSL) domain, and fuzzy intensification operators are used to control the color fidelity of the local color corrected images.Thus, is able to sort out the problem of overexposed and underexposed regions and provide optimized contrast enhancement in colored images.Several experiments have been performed to analyze the performance of the proposed method and feasibility as compared to existing techniques.Performance parameters such as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM) and Naturalness Image Quality Evaluator (NIQE) is evaluated and the comparison with some existing techniques of contrast enhancement of color images is performed.The obtained result have good contrast and approve the better performance of the proposed method in support of the quantitative measure of perceptual appearance of the processed images and low computational time.
Modality-specific Distillation

Large neural networks are impractical to deploy on mobile devices due to their heavy computational cost and slow inference.Knowledge distillation (KD) is a technique to reduce the model size while retaining performance by transferring knowledge from a large "teacher" model to a smaller "student" model.However, KD on multimodal datasets such as vision-language datasets is relatively unexplored and digesting such multimodal information is challenging since different modalities present different types of information.In this paper, we propose modality-specific distillation (MSD) to effectively transfer knowledge from a teacher on multimodal datasets.Existing KD approaches can be applied to multimodal setup, but a student doesn't have access to modality-specific predictions.Our idea aims at mimicking a teacher's modalityspecific predictions by introducing an auxiliary loss term for each modality.Because each modality has different importance for predictions, we also propose weighting approaches for the auxiliary losses; a meta-learning approach to learn the optimal weights on these loss terms.In our experiments, we demonstrate the effectiveness of our MSD and the weighting scheme and show that it achieves better performance than KD.
Analysis of the international experience of implementing blockchain technology

Background: Today, blockchain technology is of interest in both the technology and business sectors.The information technology industry is developing very rapidly in our time, and new technologies appear so often that specialists often do not have time to react to their appearance.One of the striking examples of modern information technology potentially capable of changing the organization of effective economic management is the technology of distributed ledgers.Blockchain technology is "cross-cutting", that is, one that can be applied in many areas of the economy, management, including state, municipal and social spheres.This paper determines by the provisions and the need to form the basis for the prospective assessment of the consequences of the implementation of blockchain technology, both nationally and internationally and collect information on the state of the issue, comparison of information for a transaction using blockchain technology in the supply chain of logistics, proposing the model implementation and future research and recommendations.Methods: review of literature on the introduction of blockchain technology allows us to determine the current state of research issue, to note the lack of knowledge of blockchain technology and the use of a wide range of consumers.In this regard, it is necessary to conduct an analysis of the implementation of blockchain technology from the world experience. Results: the orientation of the introduction of blockchain technology to the technological side of logistics reduces risks and significantly increases stability since it forms its practical orientation. The introduction of blockchain technology in the industry is beneficial for increasing the efficiency of the workflow, data storage, managing the supply of goods, reducing errors in the document flow and its duration, reducing the duration of the logistics cycle.Conclusions: after reviewing the international experience in implementing the blockchain, it was concluded that entrepreneurs and authorities should constantly find new ways of methodological aspects of blockchain research and mechanism of applying technologies in the modern economic system.This experience shows that using blockchain technology in combination with other technologies can significantly reduce paperwork and growth of business activity, as well as reduce costs and increase efficiency.Also, based on the analysis results, we can formulate a conclusion that logistics is an industry in which the use of blockchain technology can solve the security problem in this area, as well as simplify and reduce the cost of transporting goods through smart contracts.
Active learning and negative evidence for language identification

Language identification (LID), the task of determining the natural language of a given text, is an essential first step in most NLP pipelines.While generally a solved problem for documents of sufficient length and languages with ample training data, the proliferation of microblogs and other social media has made it increasingly common to encounter use-cases that don't satisfy these conditions.In these situations, the fundamental difficulty is the lack of, and cost of gathering, labeled data: unlike some annotation tasks, no single "expert" can quickly and reliably identify more than a handful of languages.This leads to a natural question: can we gain useful information when annotators are only able to rule out languages for a given document, rather than supply a positive label?What are the optimal choices for gathering and representing such negative evidence as a model is trained?In this paper, we demonstrate that using negative evidence can improve the performance of a simple neural LID model.This improvement is sensitive to policies of how the evidence is represented in the loss function, and for deciding which annotators to employ given the instance and model state.We consider simple policies and report experimental results that indicate the optimal choices for this task.We conclude with a discussion of future work to determine if and how the results generalize to other classification tasks.
The More Detail, the Better? – Investigating the Effects of Semantic Ontology Specificity on Vector Semantic Classification with a Plains Cree / nêhiyawêwin Dictionary

One problem in the task of automatic semantic classification is the problem of determining the level on which to group lexical items.This is often accomplished using already existing, hierarchical semantic ontologies.The following investigation explores the computational assignment of semantic classifications on the contents of a dictionary of nêhiyawêwin / Plains Cree (ISO: crk, Algonquian, Western Canada and United States), using a semantic vector space model, and following two semantic ontologies, WordNet and SIL's Rapid Words, and compares how these computational results compare to manual classifications with the same two ontologies.
Constructing Crop Portraits Based on Graph Databases Is Essential to Agricultural Data Mining

Neo4j is a graph database that can use not only data, but also data relationships. Crop portraits, a kind of property graph, model the crop entity in the real world based on data to realize the networked management of crop knowledge. The existing crop knowledge base has shortcomings such as single crop variety, incomplete description, and lack of agricultural knowledge. Constructing crop portraits can provide a comprehensive description of crops and make up for these shortcomings. This research used agricultural question-and-answer data and popular science data obtained by text crawling as the original data, selected labels to establish a crop portrait that including three categories (crops, pesticides, and diseases and pests), and used the graph database (Neo4j) to store and display these portrait data. Information mining found that the crop portrait revealed the occurrence trend of diseases and pests, exhibited a nonintrinsic connection between different diseases and pests, and provided a variety of pesticides to choose from for control of diseases and pests. The results showed that constructing crop portraits is beneficial to agricultural analysis, and has practical application values and theoretical research prospects in the field of big data analytics.
Discovering Computer Science Research Topic Trends using Latent Dirichlet Allocation

Before conducting a research project, researchers must find the trends and state of the art in their research field. However, that is not necessarily an easy job for researchers, partly due to the lack of specific tools to filter the required information by time range. This study aims to provide a solution to that problem by performing a topic modeling approach to the scraped data from Google Scholar between 2010 and 2019. We utilized Latent Dirichlet Allocation (LDA) combined with Term Frequency-Indexed Document Frequency (TF-IDF) to build topic models and employed the coherence score method to determine how many different topics there are for each year’s data. We also provided a visualization of the topic interpretation and word distribution for each topic as well as its relevance using word cloud and PyLDAvis. In the future, we expect to add more features to show the relevance and interconnections between each topic to make it even easier for researchers to use this tool in their research projects.
APo-VAE: Text Generation in Hyperbolic Space

Natural language often exhibits inherent hierarchical structure ingrained with complex syntax and semantics.However, most state-ofthe-art deep generative models learn embeddings only in Euclidean vector space, without accounting for this structural property of language.We investigate text generation in a hyperbolic latent space to learn continuous hierarchical representations.An Adversarial Poincaré Variational Autoencoder (APo-VAE) is presented, where both the prior and variational posterior of latent variables are defined over a Poincaré ball via wrapped normal distributions.By adopting the primal-dual formulation of Kullback-Leibler divergence, an adversarial learning procedure is introduced to empower robust model training.Extensive experiments in language modeling, unaligned style transfer, and dialog-response generation demonstrate the effectiveness of the proposed APo-VAE model over VAEs in Euclidean latent space, thanks to its superb capabilities in capturing latent language hierarchies in hyperbolic space.
A Language-aware Approach to Code-switched Morphological Tagging

Morphological tagging of code-switching (CS) data becomes more challenging especially when language pairs composing the CS data have different morphological representations.In this paper, we explore a number of ways of implementing a language-aware morphological tagging method and present our approach for integrating language IDs into a transformerbased framework for CS morphological tagging.We perform our set of experiments on the Turkish-German SAGT Treebank.Experimental results show that including language IDs to the learning model significantly improves accuracy over other approaches.
GTN-ED: Event Detection Using Graph Transformer Networks

Recent works show that the graph structure of sentences, generated from dependency parsers, has potential for improving event detection.However, they often only leverage the edges (dependencies) between words, and discard the dependency labels (e.g., nominal-subject), treating the underlying graph edges as homogeneous.In this work, we propose a novel framework for incorporating both dependencies and their labels using a recently proposed technique called Graph Transformer Networks (GTN).We integrate GTNs to leverage dependency relations on two existing homogeneousgraph-based models, and demonstrate an improvement in the F1 score on the ACE dataset.
Automatic Recognition of Communication Signal Modulation Based on the Multiple-Parallel Complex Convolutional Neural Network

This paper implements a deep learning-based modulation pattern recognition algorithm for communication signals using a convolutional neural network architecture as a modulation recognizer. In this paper, a multiple-parallel complex convolutional neural network architecture is proposed to meet the demand of complex baseband processing of all-digital communication signals. The architecture learns the structured features of the real and imaginary parts of the baseband signal through parallel branches and fuses them at the output according to certain rules to obtain the final output, which realizes the fitting process to the complex numerical mapping. By comparing and analyzing several commonly used time-frequency analysis methods, a time-frequency analysis method that can well highlight the differences between different signal modulation patterns is selected to convert the time-frequency map into a digital image that can be processed by a deep network. In order to fully extract the spatial and temporal characteristics of the signal, the CLP algorithm of the CNN network and LSTM network in parallel is proposed. The CNN network and LSTM network are used to extract the spatial features and temporal features of the signal, respectively, and the fusion of the two features as well as the classification is performed. Finally, the optimal model and parameters are obtained through the design of the modulation recognizer based on the convolutional neural network and the performance analysis of the convolutional neural network model. The simulation experimental results show that the improved convolutional neural network can produce certain performance gains in radio signal modulation style recognition. This promotes the application of machine learning algorithms in the field of radio signal modulation pattern recognition.
Educing knowledge from text: semantic information extraction of spatial concepts and places

Abstract. A growing body of geospatial research has shifted the focus from fully structured to semistructured and unstructured content written in natural language. Natural language texts provide a wealth of knowledge about geospatial concepts, places, events, and activities that needs to be extracted and formalized to support semantic annotation, knowledge-based exploration, and semantic search. The paper presents a web-based prototype for the extraction of geospatial entities and concepts, and the subsequent semantic visualization and interactive exploration of the extraction results. A lightweight ontology anchored in natural language guides the interpretation of natural language texts and the extraction of relevant domain knowledge. The approach is applied on three heterogeneous sources which provide a wealth of spatial concepts and place names.
Phenomenon Of Verbal Bullying In The Pedawang Society

This study aims to determine the forms and factors of verbal bullying in children and the impact of verbal bullying on children’s self confidence in Pedawang Village. The research method used is descriptive qualitative. The researcsubjects were three children in Pedawang Village. Based on the research result, there are two kinds of forms and factors of verbal bullying, namely verbal bullying based on nicknames and verbal bullying based on physical forms. Victims of verbal bullying become less confident about themselves, this is evidenced by the vicims tho become quiet and insecure about themselves while paling together. From the results of the study, it was also found that the occurrence of verbal bullying was only for a joke to his friend, not to hurt the feelings of other.
Peru is Multilingual, Its Machine Translation Should Be Too?

Peru is a multilingual country with a long history of contact between the indigenous languages and Spanish.Taking advantage of this context for machine translation is possible with multilingual approaches for learning both unsupervised subword segmentation and neural machine translation models.The study proposes the first multilingual translation models for four languages spoken in Peru: Aymara, Ashaninka, Quechua and Shipibo-Konibo, providing both many-to-Spanish and Spanishto-many models and outperforming pairwise baselines in most of them.The task exploited a large English-Spanish dataset for pretraining, monolingual texts with tagged backtranslation, and parallel corpora aligned with English.Finally, by fine-tuning the best models, we also assessed the out-of-domain capabilities in two evaluation datasets for Quechua and a new one for Shipibo-Konibo 1 .
News Classification Using Machine Learning

There are plenty of social media webpages and platforms producing the textual data. These different kind of a data needs to be analysed and processed to extract meaningful information from raw data. Classification of text plays a vital role in extraction of useful information along with summarization, text retrieval. In our work we have considered the problem of news classification using machine learning approach. Currently we have a news related dataset which having various types of data like entertainment, education, sports, politics, etc. On this data we have applying classification algorithm with some word vectorizing techniques in order to get best result. The results which we got that have been compared on different parameters like Precision, Recall, F1 Score, accuracy for performance improvement.
Low-Resource Spoken Language Identification Using Self-Attentive Pooling and Deep 1D Time-Channel Separable Convolutions

This memo describes NTR/TSU winning submission for Low Resource ASR challenge at Dialog2021 conference, language identification track.Spoken Language Identification (LID) is an important step in a multilingual Automated Speech Recognition (ASR) system pipeline.Traditionally, the ASR task requires large volumes of labeled data that are unattainable for most of the world's languages, including most of the languages of Russia.In this memo, we show that a convolutional neural network with a Self-Attentive Pooling layer shows promising results in low-resource setting for the language identification task and set up a SOTA for the Low Resource ASR challenge dataset.Additionally, we compare the structure of confusion matrices for this and significantly more diverse VoxForge dataset and state and substantiate the hypothesis that whenever the dataset is diverse enough so that the other classification factors, like gender, age etc. are well-averaged, the confusion matrix for LID system bears the language similarity measure.
Human-like informative conversations: Better acknowledgements using conditional mutual information

This work aims to build a dialogue agent that can weave new factual content into conversations as naturally as humans.We draw insights from linguistic principles of conversational analysis and annotate human-human conversations from the Switchboard Dialog Act Corpus to examine humans strategies for acknowledgement, transition, detail selection and presentation.When current chatbots (explicitly provided with new factual content) introduce facts into a conversation, their generated responses do not acknowledge the prior turns.This is because models trained with two contexts -new factual content and conversational history -generate responses that are non-specific w.r.t.one of the contexts, typically the conversational history.We show that specificity w.r.t.conversational history is better captured by pointwise conditional mutual information (pcmi h ) than by the established use of pointwise mutual information (pmi).Our proposed method, Fused-PCMI, trades off pmi for pcmi h and is preferred by humans for overall quality over the Max-PMI baseline 60% of the time.Human evaluators also judge responses with higher pcmi h better at acknowledgement 74% of the time.The results demonstrate that systems mimicking human conversational traits (in this case acknowledgement) improve overall quality and more broadly illustrate the utility of linguistic principles in improving dialogue agents.
Invertibility of Generalized Space-Time Autoregressive Model with Random Weight

The generalized linear process accomplishes stationarity and invertibility properties. The invertibility property must be having a series of convergence conditions of the process parameter. The generalized Space-Time Autoregressive (GSTAR) model is one of the stationary linear models therefore it is necessary to reveal the invertibility through the convergence of the parameter series. This article studies the invertibility of model GSTAR(1;1) with kernel random weight. The result shows that the model GSTAR(1;1) under kernel random weight fulfills the invertibility property and obtains a finite order of Generalized Space-Time Moving Average (GSTMA) process. The other result obtained is the time order of the finite orde . On the Triangular kernel resulted in the relatively great value n, so that it does not apply to the kernel with a finite value n.
Family of Origin and Family of Choice: Massively Parallel Lexiconized Iterative Pretraining for Severely Low Resource Text-based Translation

We translate a closed text that is known in advance into a severely low resource language by leveraging massive source parallelism.In other words, given a text in 124 source languages, we translate it into a severely low resource language using only ∼1,000 lines of low resource data without any external help.Firstly, we propose a systematic method to rank and choose source languages that are close to the low resource language.We call the linguistic definition of language family Family of Origin (FAMO), and we call the empirical definition of higher-ranked languages using our metrics Family of Choice (FAMC).Secondly, we build an Iteratively Pretrained Multilingual Orderpreserving Lexiconized Transformer (IPML) to train on ∼1,000 lines (∼3.5%) of low resource data.To translate named entities correctly, we build a massive lexicon table for 2,939 Bible named entities in 124 source languages, and include many that occur once and covers more than 66 severely low resource languages.Moreover, we also build a novel method of combining translations from different source languages into one.Using English as a hypothetical low resource language, we get a +23.9 BLEU increase over a multilingual baseline, and a +10.3 BLEU increase over our asymmetric baseline in the Bible dataset.We get a 42.8 BLEU score for Portuguese-English translation on the medical EMEA dataset.We also have good results for a real severely low resource Mayan language, Eastern Pokomchi.