Reviewerly/exHarmony huggingface dataset:
Kubernetes cluster optimization using hybrid shared-state scheduling framework

This paper presents a novel approach for scheduling the workloads in a Kubernetes cluster, which are sometimes unequally distributed across the environment or deal with fluctuations in terms of resources utilization. Our proposal looks at a hybrid shared-state scheduling framework model that delegates most of the tasks to the distributed scheduling agents and has a scheduling correction function that mainly processes the unscheduled and unprioritized tasks. The scheduling decisions are made based on the entire cluster state which is synchronized and periodically updated by a master-state agent. By preserving the Kubernetes objects and concepts, we analyzed the proposed scheduler behavior under different scenarios, for instance we tested the failover/recovery behavior in a deployed Kubernetes cluster. Moreover, our findings show that in situations like collocation interference or priority preemption, other centralized scheduling frameworks integrated with the Kubernetes system might not perform accordingly due to high latency derived from the calculation of load spreading. In a wider context of the existing scheduling frameworks for container clusters, the distributed models lack of visibility at an upper-level scheduler might generate conflicting job placements. Therefore, our proposed scheduler encompasses the functionality of both centralized and distributed frameworks. By employing a synchronized cluster state, we ensure an optimal scheduling mechanism for the resources utilization.
Big Data Processing Platform on Intelligent Transportation Systems

Nowadays, with the incredible demographic explosion that we have witnessed in the last few decades, management of transport is of paramount importance.The reason for this is that we have to face the management of problems relating to traffic detection, traffic jams created by urban public transport, data on motorway tolls, meteorological data and traffic safety, etc.These types of traffic data are numerous and enormous.Traditional tools are now unable to solve these problems.With the rapid development of Big Data technologies, the new way of thinking about intelligent transport has become an obligation; as a result, new architectures are mainly needed to work with big data. .In order to overcome this problem, it is essential to create a Big Data modeling approach for ITS, which pays particular attention to the creation of multiple layers.Among these we find Management and Processing layer which in its turn contains three levels: processing, analyzing and storing.In this paper, we are interested in the processing level, which attracts the attention of researchers.In fact, we will propose a Big Data processing design applied to Intelligent Transportation Systems.We will adopt a data modeling approach that treats both the transmission and the processing data.
Evaluation and Improvement of Chatbot Text Classification Data Quality Using Plausible Negative Examples

We describe and validate a metric for estimating multi-class classifier performance based on cross-validation and adapted for improvement of small, unbalanced natural-language datasets used in chatbot design.Our experiences draw upon building recruitment chatbots that mediate communication between job-seekers and recruiters by exposing the ML/NLP dataset to the recruiting team.Evaluation approaches must be understandable to various stakeholders, and useful for improving chatbot performance.The metric, nex-cv, uses negative examples in the evaluation of text classification, and fulfils three requirements.First, it is actionable: it can be used by non-developer staff.Second, it is not overly optimistic compared to human ratings, making it a fast method for comparing classifiers.Third, it allows model-agnostic comparison, making it useful for comparing systems despite implementation differences.We validate the metric based on seven recruitmentdomain datasets in English and German over the course of one year.
A Content-Based Recommendation Framework for Judicial Cases

Under the background of the Judicial Reform of China, big data of judicial cases are widely used to solve the problem of judicial research. Similarity analysis of judicial cases is the basis of wisdom judicature. In view of the necessity of getting rid of the ineffective information and extracting useful rules and conditions from the descriptive document, the analysis of Chinese judicial cases with a certain format is a big challenge. Hence, we propose a method that focuses on producing recommendations that are based on the content of judicial cases. Considering the particularity of Chinese language, we use “jieba” text segmentation to preprocess the cases. In view of the lack of labels of user interest and behavior, the proposed method considers the content information via adopting TF-IDF combined with LDA topic model, as opposed to the traditional methods such as CF (Collaborative Filtering Recommendations). Users are recommended to compute cosine similarity of cases in the same topic. In the experiments, we evaluate the performance of the proposed model on a given dataset of nearly 200,000 judicial cases. The experimental result reveals when the number of topics is around 80, the proposed method gets the best performance.
Recognition of Printed Mathematical Formula Symbols Based on Convolutional Neural Network

Printed mathematical formula recognition is a topic research region in OCR. But the diversity of fonts and sizes of mathematical symbols, as well as incorrect segmentation and stroke destruction of touching symbols lead to the difficulty of feature extraction and low recognition rate. In this paper, we constructed a convolutional neural network to recognize formula symbols, and determined the optimal parameters of the network through a large number of comparative experiments. Two convolution layers and sampling layers deepened the number of network layers and improved the recognition rate to a certain extent. Convolution kernels with fixed size extracted gradient information effectively, and ReLU activation function and dropout connection mode reduced the degree of over-fitting and gained the better generalization ability of the network. The experimental results show that the presented method can improve the recognition of printed formula symbols.
Word Segmentation for Chinese Judicial Documents

Word segmentation is an integral step in many knowledge discovery applications. However, existing word segmentation methods have problems when applying to Chinese judicial documents: (1) existing methods rely on large-scale labeled data which is typically unavailable in judicial documents, and (2) judicial document has its own language features and writing formats. In this paper, a word segmentation method is proposed for Chinese judicial documents. The proposed method consists of two steps: (1) automatically generating some labeled data as legal dictionaries, and (2) applying a hybrid multi-layer neural networks to do word segmentation incorporating legal dictionaries. Experiments are conducted on a dataset of Chinese judicial documents showing that the proposed model can achieve better results than the existing methods.
Discovering multiword expressions

Abstract In this paper, we provide an overview of research on multiword expressions (MWEs), from a natural language processing perspective. We examine methods developed for modelling MWEs that capture some of their linguistic properties, discussing their use for MWE discovery and for idiomaticity detection. We concentrate on their collocational and contextual preferences, along with their fixedness in terms of canonical forms and their lack of word-for-word translatatibility. We also discuss a sample of the MWE resources that have been used in intrinsic evaluation setups for these methods.
What do phone embeddings learn about Phonology?

Recent work has looked at evaluation of phone embeddings using sound analogies and correlations between distinctive feature space and embedding space. It has not been clear what aspects of natural language phonology are learnt by neural network inspired distributed representational models such as word2vec. To study the kinds of phonological relationships learnt by phone embeddings, we present artificial phonology experiments that show that phone embeddings learn paradigmatic relationships such as phonemic and allophonic distribution quite well. They are also able to capture co-occurrence restrictions among vowels such as those observed in languages with vowel harmony. However, they are unable to learn co-occurrence restrictions among the class of consonants.
Comparative Study of The Performance of Various Classifiers in Labeling Non-Functional Requirements

Software systems are to be developed based on expectations of customers. These expectations are expressed using natural languages. To design a software meeting the needs of the customer and the stakeholders, the intentions, feedbacks and reviews are to be understood accurately and without ambiguity. These textual inputs often contain inaccuracies, contradictions and are seldom given in a well-structured form. The issues mentioned in the previous thought frequently result in the program not satisfying the expectation of the stakeholders. In particular, for non-functional requirements, clients rarely emphasize these specifications as much as they might be justified. Identifying, classifying and reconciling the requirements is one of the main duty of the System Analyst, which task, without using a proper tool, can be very demanding and time-consuming. Tools which support text processing are expected to improve the accuracy of identification and classification of requirements even in an unstructured set of inputs. System Analysts can use them also in document archeology tasks where many documents, regulations, standards, etc. have to be processed. Methods elaborated in natural language processing and machine learning offer a solid basis, however, their usability and the possibility to improve the performance utilizing the specific knowledge from the domain of the software engineering are to be examined thoroughly. In this paper, we present the results of our work adapting natural language processing and machine learning methods for handling and transforming textual inputs of software development. The major contribution of our work is providing a comparison of the performance and applicability of the state-of-the-art techniques used in natural language processing and machine learning in software engineering. Based on the results of our experiments, tools can be designed which can support System Analysts working on textual inputs.
Exact Inference with Approximate Computation for Differentially Private Data via Perturbations

This paper discusses how two classes of approximate computation algorithms can be adapted, in a modular fashion, to achieve exact statistical inference from differentially private data products. Considered are approximate Bayesian computation for Bayesian inference, and Monte Carlo Expectation-Maximization for likelihood inference. Up to Monte Carlo error, inference from these algorithms is exact with respect to the joint specification of both the analyst's original data model, and the curator's differential privacy mechanism. Highlighted is a duality between approximate computation on exact data, and exact computation on approximate data, which can be leveraged by a well-designed computational procedure for statistical inference.
A Recognition Algorithm for Modulation Schemes by Convolution Neural Network and Spectrum Texture

The recognition of modulation schemes for communication signals is an important part of communication surveillance and spectrum monitoring. An algorithm based on deep learning and spectrum texture is proposed to recognize modulation schemes. Based on imperceptible differences among various spectrums of modulation schemes, the algorithm uses Convolution Neural Network to capture the features of image texture and thus classify the features with a SOFTMAX classifier. The experiment shows the algorithm performs better than traditional algorithm based on feature parameters, while the features captured can better reveal the signal detail and reduces effort on feature parameter design.
Hybrid Machine Translation by Combining Output from Multiple Machine Translation Systems

This paper aims to combine output from various machine translation (MT) systems so that the overall translation quality of the source text would increase.Applicability of the developed methods for small, morphologically rich and under-resourced languages is evaluated, especially Latvian and Estonian.Existing methods have been analysed, and several combinations of methods have been proposed.The proposed methods have been implemented and evaluated using automatic and human evaluation.During this research novel methods have been created that structure source language sentences into linguistically motivated fragments and combine them using a character level neural language model; combine neural machine translation output by employing sourcetranslation attention alignments; use a multi-pass approach to produce additional incrementally improving training data.The key results of this research are new state-of-the-art machine translation systems for English ↔ Estonian; approaches for utilising neural MT generated attention alignments for MT combination and comprehension of resulting translations; MT combination systems for combining output from English → Latvian statistical MT.A practical application of the methods is implemented and described.
Local Excitation Network for Restoring a JPEG-Compressed Image

Joint photographic experts group (JPEG) compression is lossy compression, and degradation of image quality worsens at high compression ratios. Therefore, a reconstruction process is required for a visually pleasant image. In this paper, we propose an end-to-end deep learning architecture for restoring JPEG images with high compression ratios. The proposed architecture changes a core principle of the squeeze and excitation network for low-level vision tasks where pixel-level accuracy is important. Instead of extracting global features, our network extracts locally embedded features and fine-tunes each feature value by using depthwise convolution. To reduce the computational complexity and parameters with large receptive fields, we use a combination of the recursive structure and feature map down- and up-scaling processes. We also propose a compact version of the proposed model by decreasing the number of filters and simplifying the network, which has about one-twentieth of the parameters of the baseline model. Experimental results reveal that our network outperforms conventional networks quantitatively, and the restored images are clear with sharp edges and smooth blocking boundaries. Furthermore, the compact model shows higher objective results while maintaining a low number of parameters. In addition, at a high compression ratio, the overall information, including details in the blocks, are lost owing to high quantization errors. We apply a generative adversarial network structure to restore these highly damaged blocks, and the results reveal that the image produced has details similar to those of the ground truth.
An integrated Fuzzy AHP-VIKOR method for Gold Potential Mapping in Saqez prospecting zone, Iran

Mineral prospectivity mapping (MPM) is a Multi-Criteria Decision Making (MCDM) task that prioritizes mineralized areas from high to low potential through methodologies that deal with data fusion problems. The primary purpose of this research is to produce a mineral potential map of the Saqez area in Kurdistan, Western Iran, to delimit the promising areas for subsequent studies in gold exploration. This area is well-known for its orogenic gold occurrences where several deposits/prospects have been discovered recently. To seek blind targets in the region, three evidential criteria, including geological, remote sensing, and geochemical layers, were used to generate the gold potential map of the Saqez area. The technique used to gain the weights of evidential layers is the fuzzy analytical hierarchy process (fuzzy AHP). Regarding this technique, at least three expert decision-makers (DMs) are required to run the method, where the results have superiority compared to the conventional AHP method with high values of uncertainty. The obtained weights of criteria were used to integrate all indicator layers incorporated in geospatial datasets through three methods, including conventional VIKOR, modified VIKOR, and multi-class index overlay. In order to validate the final MPMs, the predication ratio of seven gold prospects along with favorable zones were calculated as an efficiency index, where it was 85.65, 85.29, and 78.47 %, respectively. It proved the applicability of the VIKOR approach in gold potential mapping in comparison to the multi-class index overlay. The southwestern portions of the prospecting zone were highlighted as the most favorable potential area for gold occurrences.
A comparative study on wavelets and residuals in deep super resolution

Despite the advances in single-image super resolution using deep convolutional networks, the main problem remains unsolved: recovering fine texture details. Recent works in super resolution aim at modifying the training of neural networks to enable the recovery of these details. Among the different method proposed, wavelet decomposition are used as inputs to super resolution networks to provide structural information about the image. Residual connections may also link different network layers to help propagate high frequencies. We review and compare the usage of wavelets and residuals in training super resolution neural networks. We show that residual connections are key in improving the performance of deep super resolution networks. We also show that there is no statistically significant performance difference between spatial and wavelet inputs. Finally, we propose a new super resolution architecture that saves memory costs while still using residual connections, and performing comparably to the current state of the art.
Analysing Students’ Creativity in Integrating GeoGebra Applets in Solving Geometrical Problems

The challenges facing the economy and technologies determine the needs of the labor market for creative professionals, competent specialists, easily adaptive to the technologically changing information environment.Universities are confronted with the main task to experiment and to discover new opportunities for stimulating student creativity, for teaching and learning for mastering specific knowledge, for developing skills and competencies as a key element in their preparation for professional growth.The authors' research investigates the creative environment for teaching and learning of students (future Mathematics and Informatics teachers in high schools), namely the developing and integrating of GeoGebra applets for solving geometric problems.The opportunities which the attractive GeoGebra software provides for training in Mathematics are used in the experimental survey.Quantitative and qualitative analyses of the results are presented.
An Efficient, Robust, and Customizable Information Extraction and Pre-processing Pipeline for Electronic Health Records

Electronic Health Records (EHR) containing large amounts of patient data present both opportunities and challenges to industry, policy makers, and researchers. These data, when extracted and analyzed effectively, can reveal critical factors that can improve clinical practices and decisions. However, the inherently complex, heterogeneous and rapidly evolving nature of these data make them extremely difficult to analyze effectively. In addition, Protected Health Information (PHI) containing sensitive yet valuable information for clinical research must first be anonymized. In this paper we identify current challenges with obtaining and pre-processing information from EHR. We then present a comprehensive, efficient “pipeline” for extracting, de-identifying, and standardizing EHR data. We demonstrate the use of this pipeline, based on software from EPIC Systems, in analysing chronic kidney disease, prostate cancer, and cardiovascular disease. We also address challenges associated with temporal laboratory time series data and natural text data and develop a novel approach for clustering irregular Multivariate Time Series (MTS). The pipeline organizes data into a structured, machine-readable format which can be effectively applied in clinical research studies to optimize processes, personalize care, and improve quality, and outcomes.
CHARTING TOPOGRAPHIC MAPS BASED ON UAV DATA USING THE IMAGE CLASSIFICATION METHOD

A topographic map is a representation of the terrain, its landform and spatial elements present therein.Land surveying and photogrammetric measurements must be conducted in order to produce such cartographic document.The following must be done while obtaining information on topographic objects: determine the character and type of an object or phenomenon; determine the range of its occurrence; indicate a precise location.The next stage involves classification of objects into relevant classes and categories, i.e. arable land, pastures, forests, water basins, technical infrastructure, buildings, and other.Then, the determined classes undergo the process of cartographic generalization by combining smaller elements into a single complex, determination of a common border of their occurrence, and application of relevant graphic symbols and colours.The measuring technique which provides quick and accurate topographic information about the surrounding area is the one that uses Unmanned Aerial Vehicles (UAV).Digital photographs taken during the flight are the basis for generating a high-quality orthophotomap.Accurate determination of the location of individual spatial elements allows large-scale cartographic documents to be developed.This paper will present the method of charting topographic maps of rural areas based on orthophotomaps made from the photographs taken during the UAV flight.Supervised and unsupervised methods of object classification will be tested in order to increase the effectiveness of determination of types and occurrence range of individual topographic objects, and the obtained results will be used to chart a topographic map of the studied area.
Sentiment Analysis for Arabizi: Application to Algerian Dialect

Sentiment Analysis and its applications have spread to many languages and domains. With regard to Arabic and its dialects, we witness an increasing interest simultaneously with increase of Arabic texts volume in social media. However, the Algerian dialect had received little attention, and even less in Latin script (Arabizi). In this paper, we propose a supervised approach for sentiment analysis of Arabizi Algerian dialect using different classifiers such as Naive Bayes and Support Vector Machines. We investigate the impact of several preprocessing techniques, dealing with dialect specific aspects. Experimental evaluation on three manually annotated datasets shows promising performance where the approach yielded the highest classification accuracy using SVM algorithm. Moreover, our results emphasize the positive impact of proposed preprocessing techniques. The adding of vowels removal and transliteration, to overcome phonetic and orthographic varieties, allowed us to lift the F-score of SVM from 76 % to 87 %, which is considerable.