Reviewerly/exHarmony huggingface dataset:
A Study in the Automation of Service Ticket Recognition using Natural Language Processing

Natural language processing (NLP) is a branch of computer science concerned with the understanding of human language and communication, and translating these into a computer-comprehensible embedding. Our goal in this paper is to capture meaning from human natural language through NLP and provide an automated solution for aiding the process of service ticket solving, through the intelligent classification of tickets, pattern recognition and similarities between texts. The difficulty of this task lies in translating the human language into a mathematical format: transforming a non-formal language, into a formal one, without losing any details. Also what raises even more complication is the context in which this language appears: service tickets, that come from a technical and specialized jargon of computer science and IT industry, and the brief manner in which the tickets are written. This paper aims to tackle this challenge through multiple methods of text classification and recognition, and data analysis, followed by comparison and interpretation of the results. In completion, we find that our methods yield plausible results to be implemented in helping the service process.
Semi-Supervised Seq2seq Joint-Stochastic-Approximation Autoencoders With Applications to Semantic Parsing

Developing Semi-Supervised Seq2Seq (S4) learning for sequence transduction tasks in natural language processing (NLP), e.g. semantic parsing, is challenging, since both the input and the output sequences are discrete. This discrete nature makes trouble for methods which need gradients either from the input space or from the output space. Recently, a new learning method called joint stochastic approximation is developed for unsupervised learning of fixed-dimensional autoencoders and theoretically avoids gradient propagation through discrete latent variables, which is suffered by Variational Auto-Encoders (VAEs). In this letter, we propose seq2seq Joint-stochastic-approximation AutoEncoders (JAEs) and apply them to S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> learning for NLP sequence transduction tasks. Further, we propose bi-directional JAEs (called bi-JAEs) to leverage not only unpaired input sequences (which is most commonly studied) but also unpaired output sequences. Experiments on two benchmarking datasets for semantic parsing show that JAEs consistently outperform VAEs in S4 learning and bi-JAEs yield further improvements.
Development and Evaluation of Smartphone Learning Material for Blended Language Learning

This study evaluated a newly developed smartphone-based review material application (in blended learning) that enables learners to learn continuously and intermittently by providing visualization of learning status in the smartphone application. In beginning Chinese language classes, our blended learning practice consists of both face-to-face classroom and home assignments in e-learning. For blended learning, we provided a prototype of smartphone-based learning material in 2017 but found some issues that might have prevented learning's continuance. Therefore, we developed the smartphone-based learning material application KoToToMo Plus. This version of the application enables students to learn easily in a little spare time by having achieved a user interface that provides visualization of learning status and makes it easy to resume interrupted learning and to select the next content. Since April 2018, we have practiced blended learning using and evaluating this application in actual classes. Results showed increased numbers of learning times and decreased feelings about learning as burdensome.
A method of paired zeroing of numbers in a residue system

Abstract The paper studies the problem of determining the lower boundary of number correction speed in computing systems operating in the basis of non-positional arithmetic of residual classes. The topicality of the problem is necessitated by the search of methods allowing reducing the time for digital processing of signals in non-positional neuroprocessors. The study considers several variants of error correction with a single and multiple control bases of the residue system. The calculations were performed that allowed determining the time necessary for zeroing operation. The method of paired zeroing of numbers in a system of residual classes was modified. It was demonstrated that the suggested solutions allow appreciably reducing the time consumption by digital processing of signals in neuroprocessors destined for operations of summation and multiplication.
Guiding Assistive-Technology Adaptations Through Intelligent Stream Mining of Patient Data

Users with cognitive impairments use assistive technology as part of a treatment plan. As the AT interface is manipulated, data stream mining techniques are used to monitor user goals. In this context, data mining aids caregivers in tracking user behaviors as they attempt to achieve their goals. Divergences over consecutive stream-mined models identify potential changes in user goal attainment, as the user learns his or her personalized emailing system. When a data-mined model diverges significantly from recent models, the user's behavior is flagged as a significant behavioral change. The specific changes in behavior are then characterized by analyzing model divergence as well as the underlying data. This chapter describes how divergence analysis of decision-tree and hidden Markov models can aid recognition and diagnoses of behavioral changes in support of AT adaptation, in a case study of cognitive AT for emailing. The technique may be more widely applicable to other behavior monitoring contexts.
Recognition and Pseudonymization of Data Privacy Relevant Areas in Videos for Compliance with GDPR

For the validation of sensors and systems for automated driving, videos are required and recorded. They thereby form an important building block in the development and testing of automated driving. In the last years, due to the General Data Protection Regulation of the EU, the usage of video has seen backlash from company internal compliance departments, as well as from the general public. Often, the video recorded of the external view includes features that concern the privacy of uninvolved third parties. These features include, but are not limited to, faces and license plates. In this paper, we present an approach to process these sensitive areas in the recorded videos. They are detected using state-of-the-art object detection and then made pseudonymous in a way that can be reverted if the video is used in a specially protected viewing or development environment. This way, the development of functions or systems that rely on these personal features can still be done. For all other use cases, the videos can be used without privacy concerns.
Design and Implementation of a Pipelined Median Filter Architecture

Today, image processing applications are frequently chosen in parallel with technological developments in areas such as security, health and traffic control. The most important criterion for used applications in these areas is to ensure that the system operates at high speed and in real time. Thereby, FPGAs are frequently used in such applications. An image processing algorithm is implemented in an FPGA-based development kit in this work. The ZedBoard Znyq-7000 development kit produced by Xilinx Company is used. The median filter as a nonlinear operator utilized for an oriented anisotropic diffusion function to reduce noise and preserve the edges, is performed on medical images. A pipelined architecture is proposed and developed. Experimental results show the filter efficiency in terms of processing time and image quality. The median filter runs at 81.1 μs for a human chest radiology of size 100×100 with a PSNR of 35.07 dB.
Digital Economy and Anti-Corruption: New Digital Models

The authors of this paper consider promising areas of the corruption prevention using the latest digital technologies: Blockchain, Internet of Things, Artificial Intelligence and Big Data. The purpose of this research is the analysis of advantages of the digital economy development in terms of solving social problems and crime prevention. The authors also show functional digital models of the anti-corruption compliance are defined. In addition, the research results include the determination of some shortcomings of the proposed models associated with the imperfection of the current legislation.
Accreditation of educational activities in the aspect of international quality assurance standards

The article considers the peculiarities of the Russian national educational space and legislation in the aspect of international (European) standards of quality assurance of higher education ESG (European Standards and Guidelines). The official materials and statistical data of the Executive Agency for Educational and Cultural Programs of the European Commission, given in the final report on the realization and implementation of the principles of the Bologna Process “The European Higher Education Area in 2018”, are analyzed. The main points where the procedures of the Russian state accreditation of educational activities in accordance with the requirements of the Federal State Educational Standards of Higher Education (FSES HE) and the European international (professional-public) accreditation of standards and recommendations for quality assurance of higher education in the European space ESG are reflected and their main distinguishing features are indicated.
Decision making system vocational high school election using promethee method

Abstract Vocational High School (SMK) is currently a good choice to continue Reviews their studies, programs offered by vocational schools are one of the factors of student interest, and this makes it difficult, for some parents to determine the right school for Reviews their children. Data and Information as a whole about Vocational Schools can be Obtained through the websites of each Vocational School but it is difficult, to compare them all together, because each has different achievements and advantages. Also, parents still do not know what factors are considered in choosing a vocational school and the tight competition in the world of education to determine the best for Reviews their children in accordance with Reviews their competence and parental conditions. The purpose of this study is to provide information and alternatives in the form of rankings in order to solve problems in the selection of Vocational High School so that it can help parents and the community to choose the right school for Reviews their children. The method used is Promethee as a model for making Recommendations for the selection of Vocational High School education places. The criteria used in the selection of high school vocational education places are location, facilities, number of achievements, school environment, and extracurricular activities. Results the recommendation system for selecting high school vocational education places using the Promethee method can provide information and provide alternatives to solve problems in the selection of secondary vocational schools.
A reduction formula for Waring numbers through generalized Paley graphs

We give a reduction formula for the Waring number g(k, q) over a finite field $$\mathbb {F}_q$$ . By exploiting the relation between g(k, q) with the diameter of the generalized Paley graph $$\Gamma (k,q)$$ and by using the characterization due to Pearce and Praeger (2019) of those $$\Gamma (k,q)$$ which are Cartesian decomposable, we obtain the reduction formula $$\begin{aligned} g(\tfrac{p^{ab}-1}{bc},p^{ab}) = b g(\tfrac{p^a-1}{c},p^a) \end{aligned}$$ for p prime and a, b, c positive integers under certain arithmetic conditions. Then, we find some arithmetic conditions to apply the formula above, which allow us to obtain many infinite families of explicit values of Waring numbers. Finally, we use the reduction formula together with the characterization of 2-weight irreducible cyclic codes due to Schmidt and White (2002) to find infinite families of explicit even values of g(k, q).
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Given a graph H, a graphic sequence π is potentially H-graphic if there is a realization of π containing H as a subgraph. In 1991, Erdős, Jacobson and Lehel introduced the following problem: determine the minimum even integer σ(H,n) such that each n-term graphic sequence with sum at least σ(H,n) is potentially H-graphic. This problem can be viewed as a "potential" degree sequence relaxation of the Turán problems. For an arbitrary graph H of order k, Ferrara et al. (2016) established an upper bound on σ(H,n) as follows: If ω=ω(n) be an increasing function that tends to infinity with n, then there exists an N=N(ω,H) such that σ(H,n)≤σ˜(H)n+ω(n) for any n≥N, where σ˜(H) is a parameter only depending on the graph H. In this paper, we obtain a new upper bound on σ(H,n) so that ω(n)=k2−3k+4, that is, there exists an M=M(k,α(H)) such that σ(H,n)≤σ˜(H)n+k2−3k+4 for any n≥M.
Computing Height-Optimal Tangles Faster

We study the following combinatorial problem. Given a set of n y-monotone wires, a tangle determines the order of the wires on a number of horizontal layers such that the orders of the wires on any two consecutive layers differ only in swaps of neighboring wires. Given a multiset L of swaps (that is, unordered pairs of numbers between 1 and n) and an initial order of the wires, a tangle realizes L if each pair of wires changes its order exactly as many times as specified by L. The aim is to find a tangle that realizes L using the smallest number of layers. We show that this problem is NP-hard, and we give an algorithm that computes an optimal tangle for n wires and a given list L of swaps in $$O((2|L|/n^2+1)^{n^2/2} \cdot \varphi ^n \cdot n)$$ time, where $$\varphi \approx 1.618$$ is the golden ratio. We can treat lists where every swap occurs at most once in $$O(n!\varphi ^n)$$ time. We implemented the algorithm for the general case and compared it to an existing algorithm. Finally, we discuss feasibility for lists with a simple structure.
A Two-level Cloud Storage System Based on Asynchronous Message for Medical Image Big Data

Medical imaging has rapidly become a big data problem based on the fact that there are approximately petabyte level of medical images in all the repositories. These data are managed by picture archiving and communication systems in a central high performance disk array that healthcare organizations have to pay an expensive price for storage. It's very important that patient data is collected correctly in a reliable storage media for an indefinite period of time. An economical solution will be leveraging the cost-efficient commercial cloud computing services (i.e. Amazon EC2, Microsoft Azure, Google Cloud Platform, AliCloud, etc.) to host data and conduct required analysis on demand. However, the data should be quickly accessible at any given time in clinical practice that the medical applications can access and render the data to doctors in real time. Therefore, it is impractical for radiologists to wait for a few minutes to download images from public cloud storage. As we all know that the cloud storage system usually is not good at real time scenery applications and the privacy issues should also be considered for medical data in public cloud. This paper proposed a new two-level private cloud storage system based on asynchronous message for medical image big data and the system is developed on Hadoop distributed file system. Two innovative technologies are presented that are message queue based medical image storage and online hot data pooling. The experimental results show that the system has superior real-time performance to support the front-end applications.
Estimating the Maintenance Effort of JavaScript Applications

Successful software project survival and progress over time is highly dependent on effectively managing the maintenance process. Estimating accurately maintenance process factors like the maintenance effort and the level of changes required for a new release is considered a crucial task for allocating resources. In this work we examine the maintenance process factors of JavaScript applications, which at the moment are understudied despite the need of language specific maintenance models. Furthermore we propose two maintenance indices for estimating the changes and the effort required for maintaining JavaScript applications by considering a variety of maintenance drivers. We evaluated the proposed indices through a case study on 5,788 releases coming from 60 popular JavaScript applications. The results show that project activity factors (i.e., number of open bugs and number of corrective maintenance activities) are important maintenance drivers. The proposed indices are evaluated in terms of predictive and discriminative power and both achieve high accuracy.
Emotion Recognition in Poetry Using Ensemble of Classifiers

The poetic language is more expressive compared to ordinary text, which presents more challenges for emotion recognition. This paper presents an innovative approach to recognize emotion from poems. The proposed work is the first attempt to identify emotions from English poems. To accomplish the objective, we have created a corpus Poem Emotion Recognition Corpus [PERC] consists of 736 prominent poems of various poets. In this work, the emotion is classified into nine categories such as Love, Anger, Hate, Sadness, Joy, Surprise, Peace, Courage and Fear based on Indian classical Navarasa. The proposed emotion recognition model uses a novel ensemble classifier schema based on SVM, Logistic regression, NB Classifier, and Emotion Modifier Preserved Vector Space Model. The results show that the proposed model achieves satisfactory precision, Recall and F-measure in recognizing emotions from poems.
Trustworthy Cloud Federation Through Cooperative Game Using QoS Assessment

Growing demand for cloud applications has imposed a great challenge for users in finding a suitable cloud service providers. Trustworthy cloud computing has been a promising solution for effectively judging a secure provider by monitoring the behavior of the environment. However, designing an efficient mechanism for identifying a trustworthy cloud service provider has been a problem in this heterogeneous cloud. In this paper, we address the problem through the game theoretic approach by classifying the providers, based on how they cooperate to form a coalition. Trustable characteristics like sharing of information, the formation of network community, and other quality of service parameters ensure a highly trustworthy service. These functions form the element of cooperative game theory for sharing the payoffs in a manner that a federation through coalition gets benefited. The model proves that there exists an equilibrium, where the property of core, exits and hence, any mistrusted members wishing to break the coalition are nullified.
Healthcare Expenditure Prediction for Crowd with Co-existing Medical Conditions

Healthcare has been one of the most important issues in modern society. However, expenditure on healthcare is always a high burden for both individuals and countries. Aiming to take full advantage of the limited budget of healthcare expenditure, many medical institutions resort to estimate the future healthcare cost and carefully decide their spending plans. This enlightens us that the accurate prediction of healthcare cost in the future contributes to the effective utilization of the medical resources. In this paper, we propose a healthcare expenditure prediction system for the crowd with co-existing medical conditions. We analyze the diagnoses and payment records, and leverage a gated recurrent unit (GRU) network model to learn the contextual features which would contribute to the prediction. Evaluations over diagnoses from 37,734 patients and the financial report from a real-world medical institution indicate that our system can provide dependable prediction of healthcare expenditure and it is practical to use our system to assist the medical budget decision.
Object Positioning by Visual Servoing Based on Deep Learning

Visual servoing is capable of positioning robots based on images captured by cameras. To calculate the command value for robots, hand-designed image features and extraction of image features are required. Positioning accuracy is significantly influenced by the selection of the image features. In this study, we focus on the ability of convolutional neural networks (CNN) to extract features from images and output the angular velocity to control a manipulator. We propose a visual servoing technique based on CNN enabling the precise positioning of a texture less object grasped by a parallel gripper. The positioning can be achieved even the grasping position is different from the position when the target image was captured. The positioning accuracy of the proposed method is verified based on the positioning of an object into an alignment tray using a six-DOF manipulator. We confirmed that the proposed visual servoing technique can position an object precisely.
Identifying threats in a large company's inbox

Cyber threats in emails continue to grow. Anti-spam filters have achieved good performance, but several spam emails still pass through them. Some of them are particularly dangerous as they represent attempts to breach the security policy of the company (e.g. inducing a manager to authorize a payment towards a fraudulent bank account). In this paper we propose an automated system to detect such emails, passing through antispam filter and potentially very dangerous. Our dataset is composed of real spam emails reported, collected, and labelled as critical or not by human analysts during each day of the last year in a large company's inbox. We firstly study the characteristics of dangerous emails and then train and use different supervised machine learning classifiers to detect them. Our results highlight the main distinguishing characteristics of such emails and that (a) Support Vector Machine and Random Forest classifiers achieve the best performance; (b) the full feature set considered allows to obtain up to 97% of recall and up to 92% of precision with supervised approaches; (c) highly dangerous spam emails can be easily detected with only 21 features.
Joint category-level and discriminative feature learning networks for unsupervised domain adaptation

Unsupervised domain adaptation (UDA) aims to build a classifier for the unlabeled target domain by transferring knowledge from a well-labeled source domain. Recently deep domain adaptation methods can not effectively integrate discriminability with transferability of features, and these methods can only reduce, but not remove, the cross-domain discrepancy. To this end, this paper proposes a new domain adaptation method called Joint Category-Level and Discriminative Feature Learning Network (CDN). CDN not only achieves domain adaptation by minimizing category-level distribution discrepancy between domains but also learns discriminative feature representations via maximizing inter-category distance and selecting transferability samples simultaneously. Moreover, we develop a Transferability Weighting Module (TWM), which is based on a constructed classifier, to further strengthen the discriminability of sample’s features. The experimental results demonstrate that CDN can significantly decrease the cross-domain distribution inconsistency and further promote the classification performance.