Reviewerly/exHarmony huggingface dataset:
New Approaches In Foreign Language Teaching From Confident Students To Experienced Engineers

This article studies the problems of contemporary engineering education that should be aimed at enabling future specialists to meet demands of developing industry and society. One of the most important factors of a good and effective education is a curriculum. Unfortunately, at present there is a gap between existing curriculum and the demands for foreign language learning. The paper focuses on creating new curriculum content in teaching foreign languages that would help the students of Moscow polytechnic university acquire sound learning skills. The content of the curriculum is closely connected with students’ professional engineering training. The teacher’s role isn’t only to impart knowledge, but to facilitate the process of learning. That’s why the so called “educational trajectory” was created to develop students’ communicative skills. According to this trajectory, first year students start learning general technical vocabulary and reading rather simple technical texts. During the second stage they proceed to mastering their professional vocabulary. The next compulsory course introduces the students to “Business English” that includes such topics as company structure, management development, changing careers, describing and evaluating the products. As a result, the learning motivation of students has grown noticeably as the new curriculum meets the demands of future specialists who are to live in the age of information and digital technologies.
ORCID: Building Trusted Infrastructure

ORCID: Building Trusted Infrastructure was presented by Matthew Buys and Estelle Cheng at the ORCID China Workshop on September23, 2019.
Distance-Based Approaches to Repair Semantics in Ontology-based Data Access

In the presence of inconsistencies, repair techniques thrive to restore consistency by reasoning with several repairs. However, since the number of repairs can be large, standard inconsistent tolerant semantics usually yield few answers. In this paper, we use the notion of syntactic distance between repairs following the intuition that it can allow us to cluster some repairs close to each other. In this way, we propose a generic framework to answer queries in a more personalise fashion.
THE USE OF ROLE PLAY TO IMPROVE SPEAKING SKILL FOR GRADE XI OF SMK SANGKURIANG 2 CIMAHI

The qualitative data were analyzed by reducing the data, displaying the data, drawing conclusion and verification. The outcome of the quantitative data is the main score of the test . The validity of the research data was, effect, progression, catalytic, and dialogic validity. The reliability of the research data used three step of triangulation, the first is time triangulation, the second is investigator triangulation, and the last theoretical triangulation, finally we have to used inter-rater reliability. The the pre-test result that the mean score of the grammar in the students’ performance was only 2.0. The vocabulary mean score was 2.5, the perception mean score is 2.7, the fluency mean score was 2.2, and the pronunciation’s mean score was 2.3. Meanwhile, the mean score of the grammar in the students’ performance in the post-test was 3.0. Then, the vocabularies mean score is 3.5, the perception mean score is 3.2. the fluency mean score is 3,0, and the pronunciations mean score is 3.0. Therefore, it is very clear that there were significant improvements on the students’ speaking skill through the use of role play. Therefore, it was very clear that there were significant improvements on the students’ speaking skill through the use of role play. Keywords: improvements, role plays, speaking skill
An Analysis On Record Politeness of Pragmatic In Tinker Bell And The Great Fairy Rescue Movie

This research is a linguistic study on record politeness of pragmatic in tinker bell and great fairy rescue movie, which aims to know many types of politeness and the most dominant politeness in the film. Researchers use qualitative method and the data are analyzed based on the politeness theory by Brown and Lavinson (1987). The movie lasted 1 hour 25 minutes, including 30 conversation. The result showed that there are four politeness, they are conversation bald on record (16), positive politeness (8), negative politeness (2), and off record (2). In addition the most of dominant politeness is bald on record. It means that the bald on record is an order that is directly when a someone do something in urgent situation
An Analysis on Cervical Cancer Classification of Medical Digital Images Using Various Classifiers

In this analysis, Cervical cancer took over the place four in the world level and it is the most prevalent cancer that is affecting women. If the cancer is detected in the earlier stages it can be cured and treated successfully. And it is also the leading gynecological malignancy disease worldwide. This is a paper which presents the classification techniques of cervical cancer. And also, this paper shows the advanced feature solution approaches of cervical cancer. The dimensionality reduction technique is used for the improvement of the classifier with great accuracy. There are two categories of feature selection and they are filters and wrappers. By using all these analytic techniques, we can classify cancer and its approaches. Therefore, this paper classifies the approaches of Cervical cancer.
Using AI/ML to gain situational understanding from passive network observations

The data available in the network traffic fromany Government building contains a significant amount ofinformation. An analysis of the traffic can yield insightsand situational understanding about what is happening inthe building. However, the use of traditional network packet inspection, either deep or shallow, is useful for only a limited understanding of the environment, with applicability limited to some aspects of network and security management. If weuse AI/ML based techniques to understand the network traffic, we can gain significant insights which increase our situational awareness of what is happening in the environment.At IBM, we have created a system which uses a combination of network domain knowledge and machine learning techniques to convert network traffic into actionable insights about the on premise environment. These insights include characterization of the communicating devices, discovering unauthorized devices that may violate policy requirements, identifying hidden components and vulnerability points, detecting leakage of sensitive information, and identifying the presence of people and devices.In this paper, we will describe the overall design of this system, the major use-cases that have been identified for it, and the lessons learnt when deploying this system for some of those use-cases
In Memoriam - Professor Erwin Stein

Erwin Stein passed away on 19th December 2018 at the age of 87, a few days after he had lostconsciousness when working at his desk. He was a mentor and a dear friend to many of us in thecommunity of computational mechanics.
Analysis of Co-Occurrence Patterns in Data through Modular and Clan Decompositions of Gaifman Graphs

We argue that the existing knowledge about modular decomposition of graphs and clan decomposition of 2-structures can be put to use advantageously in a context of data analysis. We show how to obtain visual descriptions of co-occurrence patterns by employing these decompositions on possibly generalized Gaifman graphs associated to datasets. We provide both theoretical advances that connect the proposed process to other data mining aspects (namely, closed set mining), as well as implemented algorithmics leading to an open-source tool that demonstrates our approach.
Large Scale Global Optimization by Hybrid Evolutionary Computation

In management, business, economics, science, engineering, and research domains, Large Scale Global Optimization (LSGO) plays a predominant and vital role. Though LSGO is applied in many of the application domains, it is a very troublesome and a perverse task. The Congress on Evolutionary Computation (CEC) began an LSGO competition to come up with algorithms with a bunch of standard benchmark unconstrained LSGO functions. Therefore, in this paper, we propose a hybrid meta-heuristic algorithm, which combines an Improved and Modified Harmony Search (IMHS), along with a Modified Differential Evolution (MDE) with an alternate selection strategy. Harmony Search (HS) does the job of exploration and exploitation, and Differential Evolution does the job of giving a perturbation to the exploration of IMHS, as harmony search suffers from being stuck at the basin of local optimal. To judge the performance of the suggested algorithm, we compare the proposed algorithm with ten excellent meta-heuristic algorithms on fifteen LSGO benchmark functions, which have 1000 continuous decision variables, of the CEC 2013 LSGO special session. The experimental results consistently show that our proposed hybrid meta-heuristic performs statistically on par with some algorithms in a few problems, while it turned out to be the best in a couple of problems.
Organ-based Age Estimation based on 3D MRI Scans.

Individuals age differently depending on a multitude of different factors such as lifestyle, medical history and genetics. Often, the global chronological age is not indicative of the true ageing process. An organ-based age estimation would yield a more accurate health state assessment. In this work, we propose a new deep learning architecture for organ-based age estimation based on magnetic resonance images (MRI). The proposed network is a 3D convolutional neural network (CNN) with increased depth and width made possible by the hybrid utilization of inception and fire modules. We apply the proposed framework for the tasks of brain and knee age estimation. Quantitative comparisons against concurrent MR-based regression networks illustrated the superior performance of the proposed work.
PERBANDINGAN KINERJA MUTUAL K-NEAREST NEIGHBOR (MKNN) DAN K-NEAREST NEIGHBOR (KNN) DALAM ANALISIS KLASIFIKASI KELAYAKAN KREDIT

Credit feasibility analysis is important for lenders to avoid the risk among the increasement of credit applications. This analysis can be carried out by the classification technique. Classification technique used in this research is instance-based classification. These techniques tend to be simple, but are very dependent on the determination of K values. K is number of nearest neighbor considered for class classification of new data. A small value of K is very sensitive to outliers. This weakness can be overcome using an algorithm that is able to handle outliers, one of them is Mutual K-Nearest Neighbor (MKNN). MKNN removes outliers first, then predicts new observation classes based on the majority class of their mutual nearest neighbors. The algorithm will be compared with KNN without outliers. The model is evaluated by 10-fold cross validation and the classification performance is measured by Gemoetric-Mean of sensitivity and specificity. Based on the analysis the optimal value of K is 9 for MKNN and 3 for KNN, with the highest G-Mean produced by KNN is equal to 0.718, meanwhile G-Mean produced by MKNN is 0.702. The best alternative to classifying credit feasibility in this study is K-Nearest Neighbor (KNN) algorithm with K=3.Keywords: Classification, Credit, MKNN, KNN, G-Mean.
<i>Roadside Geology of Tennessee,</i> by Marcy B. Davis. Mountain Press, PO Box 2399, Missoula, MT 59806; www.mountain-press.com. 400 pages; 2019; $26 plus shipping and handling (softbound).

This volume is the latest addition to the Roadside Geology series produced by Mountain Press. The aim of these books is to help the user learn about geology from easily accessible sites and to serv...
The Right to Digital Death in the Russian Federation, EU, and USA

The purpose of this study is to identify common and specific features of the theoretical and legal foundations of the right to digital death. Today, the regulation of this right is at the initial stage, and only a few countries (USA, France, Spain) have established in their legislation the norms regulating the order of inheritance of virtual objects. The legislation of the Russian Federation does not contain any special norms, however, the latest amendments to the Civil Code of the Russian Federation, which come into force on October 1, 2019, have indicated a completely different vector of development of this sphere than that followed by other countries. The analysis of the existing legislation revealed the problems of fixing and implementing the right to digital death and suggested ways to solve them. n n
Shared memory and the Bakery algorithm

research-article Share on Shared memory and the Bakery algorithm Authors: Hagit Attiya View Profile , Jennifer L. Welch View Profile Authors Info & Claims Concurrency: the Works of Leslie LamportOctober 2019 Pages 27–46https://doi.org/10.1145/3335772.3335776Published:04 October 2019Publication History 0citation220DownloadsMetricsTotal Citations0Total Downloads220Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Understanding and Pushing the Limits of the Elo Rating Algorithm

This work is concerned with the rating of players/teams in face-to-face games with three possible outcomes: loss, win, and draw. This is one of the fundamental problems in sport analytics, where the very simple and popular, non-trivial algorithm was proposed by Arpad Elo in late fifties to rate chess players. In this work we explain the mathematical model underlying the Elo algorithm and, in particular, we explain what is the implicit but not yet spelled out, assumption about the model of draws. We further extend the model to provide flexibility and remove the unrealistic implicit assumptions of the Elo algorithm. This yields the new rating algorithm, we call $κ$-Elo, which is equally simple as the Elo algorithm but provides a possibility to adjust to the frequency of draws. The discussion of the importance of the appropriate choice of the parameters is carried out and illustrated using results from English Premier League football seasons.
Lossless video coding based on multi-frame example search and probability model optimization

This paper describes a novel lossless video coding method that directly estimates a probability distribution of image values pel-by-pel. In the estimation process, several examples, i.e. a set of pels whose neighborhoods are similar to a local texture of the target pel to be encoded, are gathered from search windows located on an already encoded area of the current frame as well as those of the previous frames. Then the probability distribution is modeled as a weighted sum of the Gaussian functions whose center positions are given by the individual examples. Furthermore, model parameters that control shapes of the Gaussian functions are numerically optimized so that the resulting coding rate can be a minimum. Simulation results indicate that the coding performance can be improved by increasing the number of reference frames.
All You Need is Ratings: A Clustering Approach to Synthetic Rating Datasets Generation

The public availability of collections containing user preferences is of vital importance for performing offline evaluations in the field of recommender systems. However, the number of rating datasets is limited because of the costs required for their creation and the fear of violating the privacy of the users by sharing them. For this reason, numerous research attempts investigated the creation of synthetic collections of ratings using generative approaches. Nevertheless, these datasets are usually not reliable enough for conducting an evaluation campaign. In this paper, we propose a method for creating synthetic datasets with a configurable number of users that mimic the characteristics of already existing ones. We empirically validated the proposed approach by exploiting the synthetic datasets for evaluating different recommenders and by comparing the results with the ones obtained using real datasets.
Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks

Semantic role labeling (SRL) involves extracting propositions (i.e. predicates and their typed arguments) from natural language sentences. State-of-the-art SRL models rely on powerful encoders (e.g., LSTMs) and do not model non-local interaction between arguments. We propose a new approach to modeling these interactions while maintaining efficient inference. Specifically, we use Capsule Networks: each proposition is encoded as a tuple of \textit{capsules}, one capsule per argument type (i.e. role). These tuples serve as embeddings of entire propositions. In every network layer, the capsules interact with each other and with representations of words in the sentence. Each iteration results in updated proposition embeddings and updated predictions about the SRL structure. Our model substantially outperforms the non-refinement baseline model on all 7 CoNLL-2019 languages and achieves state-of-the-art results on 5 languages (including English) for dependency SRL. We analyze the types of mistakes corrected by the refinement procedure. For example, each role is typically (but not always) filled with at most one argument. Whereas enforcing this approximate constraint is not useful with the modern SRL system, iterative procedure corrects the mistakes by capturing this intuition in a flexible and context-sensitive way.
Synchronisation correction‐based colour image watermarking using proximal classifier with consistency and multivariate generalised Gaussian model

In this Letter, a novel synchronisation correction-based colour image watermarking approach is proposed to resist geometric distortions. The Cb channel of the YCbCr colour host image is first decomposed by a two-level contourlet transform, and binary watermark image is embedded into the host image by modifying the lowpass coefficients. Then the multivariate generalised Gaussian distribution is used to capture the significant features of colour images, and the proximal classifier with consistency is employed to estimate geometric distortion parameters. Finally, the watermark image is extracted from the corrected watermarked image. Simulation results demonstrate that the presented approach shows excellent robustness against various common image processing attacks and geometric attacks while guaranteeing invisibility.
Redei rational functions as permutation functions and an algorithm to compute redei rational functions

Permutation polynomials have received significantly wider attention because of their potential applications in cryptosystems and various combinatorial designs. A polynomial f(x) e Fq[x] of positive degree is called a permutation polynomial if and only if f(x) induces a bijection from Fq onto itself. The rational functions over Fq[x] that yield permutations of Fq are called permutation functions. In the process of the study of necessary and sufficentt conditions for a polynomial to be permutation polynomial a special kind of polynomials gn(a; x) called Dickson polynomials are introduced by Dickson and the study of Dickson polynomials lead to the introduction of the special class of the special class of functions called Redei rational functions to be permutation functions are presented and an algorithm to compute the Redei rational functions is given which is useful in cryptosystems with Redei rational functions as trapdoor functions.
PolyRecs: Improving Page–View Rates Using Real-Time Data Analysis

In this paper, we outline our effort to enhance the page-view rates of e-content that online customers read on a popular portal in Greece. The portal, athensvoice.gr, provides continuous coverage on news, politics, science, the arts, and opinion columns and its customers generate approximately 6 million unique visits per month. Gains both in terms of advertisement and further e-content market penetration were the objectives of our effort which yielded the PolyRecs system, in production for more than a year now. In designing PolyRecs, we were primarily concerned with the use of pages in real-time and to this end, we elected to utilize five key criteria to achieve the aforementioned goals. We selected criteria for which we were able to obtain pertinent statistics without compromising performance and offered a real-time exploitation of the user page-views on the go. In addition, we were keen in realizing not only effective on-the-fly calculations of what might be interesting to the browsing individuals at specific points in time but also produce accurate results capable of improving the user-experience. The key factors exploited by PolyRecs entail features from both collaboration and content-based systems. Once operational, PolyRecs helped the news portal attain an average increase of 6.3% of the overall page-views in its traffic. To ascertain the PolyRecs utility, we provide a brief economic analysis in terms of measured performance indicators and identify the degree of contribution each of the key factors offers. Last but not least, we have developed PolyRecs as a domain-agnostic hybrid-recommendation system for we wanted it to successfully function regardless of the underlying data and/or content infrastructure.
Peningkatan Kemampuan Menggambar Teknik Siswa SMK Menggunakan Software Berbasis Computer Aided Design (CAD) Sebagai Upaya Pengembangan Kualitas Nilai Sumber Daya Manusia (SDM) Untuk Menghadapi Dunia Kerja

This program was held aimed at improving the competency of technicaldrawing of vocational students in the Municipality of Kediri. Competence in this service consists of three aspects, namely affective, cognitive, and psychomotor. This community service was held at the Design Laboratory of the Mechanical Engineering of the Polytechnic of Kediri in August to Desember 2018 with the object of service being vocational students in the Department of Mechanical Engineering and Automotive. This program is carried out with a model of meeting in the classroom, giving theory, practicum, performance of making parts with image models and real object models. The improvement of students' abilities was measured using observation sheets of student activities to determine the improvement of theaffective aspects of students, the pretest and posttest instruments to determine the increase in cognitive aspects of students, the worksheets of students to determine the improvement of psychomotor aspects. The first output of this program is to increase the ability of vocational students in the field of mechanical engineering with the help of computers using CAD-based software. The next output is a competency certificate that will be given to students in accordance with their competencies.