Reviewerly/exHarmony huggingface dataset:
Hybrid SVM and ARIMA Model for Failure Time Series Prediction based on EEMD

A more widely used hybrid model of support vector regression (SVR) and autoregressive integrated moving average (ARIMA) based on Ensemble Empirical Mode Decomposition (EEMD) is proposed for failure time series prediction by taking advantage of the SVR model to forecast the nonlinear part of failure time series and the ARIMA model to predict the linear basic part.It firstly uses EEMD to decompose the original failure sequence into several significant fluctuation components and a trend component, and then it utilizes SVR and ARIMA to forecast them separately.The performance of the presented model is measured against other unitary models such as Holt-Winters, autoregressive integrated moving average, multiple linear regression, and group method of data handling of seven published nonlinear non-stationary failure datasets.The comparison results indicate that the proposed model outperforms other techniques and can be utilized as a promising tool for failure data forecast applications.
Improving Webpage Access Predictions Based on Sequence Prediction and PageRank Algorithm

Aim/Purpose: In this article, we provide a better solution to Webpage access prediction. In particularly, our core proposed approach is to increase accuracy and efficiency by reducing the sequence space with integration of PageRank into CPT+. Background: The problem of predicting the next page on a web site has become significant because of the non-stop growth of Internet in terms of the volume of contents and the mass of users. The webpage prediction is complex because we should consider multiple kinds of information such as the webpage name, the contents of the webpage, the user profile, the time between webpage visits, differences among users, and the time spent on a page or on each part of the page. Therefore, webpage access prediction draws substantial effort of the web mining research community in order to obtain valuable information and improve user experience as well. Methodology: CPT+ is a complex prediction algorithm that dramatically offers more accurate predictions than other state-of-the-art models. The integration of the importance of every particular page on a website (i.e., the PageRank) regarding to its associations with other pages into CPT+ model can improve the performance of the existing model. Contribution: In this paper, we propose an approach to reduce prediction space while improving accuracy through combining CPT+ and PageRank algorithms. Experimental results on several real datasets indicate the space reduced by up to between 15% and 30%. As a result, the run-time is quicker. Furthermore, the prediction accuracy is improved. It is convenient that researchers go on using CPT+ to predict Webpage access. Findings: Our experimental results indicate that PageRank algorithm is a good solution to improve CPT+ prediction. An amount of though approximately 15 % to 30% of redundant data is removed from datasets while improving the accuracy. Recommendations for Practitioners: The result of the article could be used in developing relevant applications such as Webpage and product recommendation systems. Recommendation for Researchers: The paper provides a prediction model that integrates CPT+ and PageRank algorithms to tackle the problem of complexity and accuracy. The model has been experimented against several real datasets in order to show its performance. Impact on Society: Given an improving model to predict Webpage access using in several fields such as e-learning, product recommendation, link prediction, and user behavior prediction, the society can enjoy a better experience and more efficient environment while surfing the Web. Future Research: We intend to further improve the accuracy of webpage access prediction by using the combination of CPT+ and other algorithms.
An Infinitary Treatment of Full Mu-Calculus

We explore the proof theory of the modal $$\mu $$-calculus with converse, aka the ‘full $$\mu $$-calculus’. Building on nested sequent calculi for tense logics and infinitary proof theory of fixed point logics, a cut-free sound and complete proof system for full $$\mu $$-calculus is proposed. As a corollary of our framework, we also obtain a direct proof of the regular model property for the logic: every satisfiable formula has a tree model with finitely many distinct subtrees. To obtain the results we appeal to the basic theory of well-quasi-orderings in the spirit of Kozen’s proof of the finite model property for $$\mu $$-calculus without converse.
Named Entity Recognition in Tamil Language Using Recurrent Based Sequence Model

Information extraction is a key task in natural language processing which helps in knowledge discovery by extracting facts from the semi-structured text like natural language. Named entity recognition is one of the subtask under information extraction. In this work, we use recurrent based sequence models called Long Short-Time Memory (LSTM) for named entities recognition in Tamil language and word representation for words is done through a distributed representation of words. For this work, we have created a Tamil named entities recognition corpus by crawling Wikipedia and we have also used openly available FIRE-2018 Information Extractor for Conversational Systems in Indian Languages (IECSIL) shared task corpus.
University and Professional Society of Social Workers as Subjects of Personnel Training

In modern society, there is much concern about training highly qualified specialists in the field of social work. Both higher education institutions that provide this training and the professional community are interested in it. The effectiveness of the social institution activity depends on how the personnel is prepared, what competencies and the level of professionalism it possesses. Equal partnership between educational and professional communities will enable university to respond quickly to changes in the economy and the needs of the market, to train unique specialists according to professional standards. The article highlights the cooperation experience of the Department of Service, Advertising and Social Work of Sholom-Aleichem Priamursky State University with the professional community of social workers. The authors present forms and methods of the organization of social partnership as a necessary condition for training future social workers and experts in social work. Different methods of training highly qualified personnel are also shown. The authors have identified the main trajectories of interaction between the University and the professional community of social workers which are determined by the content of educational standards. They include the formation of the educational programme, the involvement of social workers in the educational process, the provision of employment, postgraduate training of personnel. The article pays special attention to the problems of profiling educational programs at universities in small regions. The authors believe that the narrow definition of educational program specialization is connected with the potential lack of demand for personnel while a shortage in specialists is maintained. The authors consider volunteering as an additional resource for the development of professional competencies in the field of social work and a channel for effective interaction between the University and the professional community.
CodeForTheChange at SemEval-2019 Task 8: Skip-Thoughts for Fact Checking in Community Question Answering

Community Question Answering (cQA) is one of the popular Natural Language Processing (NLP) problems being targeted by researchers across the globe.Couple of the unanswered questions in the domain of cQA are 'can we label the questions/answers as factual or not?' and 'Is the given answer by the user to a particular factual question is correct and if it is correct, can we measure the correctness and factuality of the given answer?'.We have participated in SemEval-2019 Task 8 which deals with these questions.In this paper, we present the features used, approaches followed for feature engineering, models experimented with and finally the results.Our primary submission with accuracy (official metric for Se-mEval Task 8) of 0.65 in Subtask B (Answer Classification) and 0.63 in Subtask A (Question Classification) stood at 6 th and 16 th places respectively.
The Problem of Bad Governance as a Determinant of State Dysfunctionality in Sub-Saharan Africa

Bad governance can very quickly ruin the economic situation of the whole country. However, in the conditions of the Western world, even though they can cause a lot of damage, more or less developed administrative apparatus is able to minimize the effects of bad decisions, and the society has a chance to rebuff the rulers in the next elections. In so-called dysfunctional states bad governance can damage an already weak country’s economy. Most researchers dealing with the issue of the state in Sub-Saharan Africa believe that, once the decolonisation was over, the power in the region was mostly taken by people whose leadership must be evaluated negatively, represented by such dictators as Jean-Bédel Bokassa, Mobutu Sese Seko or Idi Amin. The paper is trying to explain the interconnection between bad governance experienced by Sub-Saharan African countries and state dysfunctionality phenomenon from which they suffer. The author formulated the analysis and assessment of bad governance presented in this article on the basis of his own field studies carried out in African countries and research of specialist literature related to African and political studies.
ADAPTING STEM AUTOMATED ASSESSMENT SYSTEM TO ENHANCE LANGUAGE SKILLS

Enhancing language skills is a duty for many institutions, schools and universities; Information and Communications Technology is nowadays supporting language teaching, heading also to the integration of foreigners. According to previous studies, the use of a Technology Enhanced Learning Environment can foster language competences through online tests. The goal of this paper is to show and discuss different typologies of automatically evaluated questions useful to learn languages and designed with an Automatic Assessment System, already successfully used for teaching STEM disciplines: Science, Technology, Engineering and Mathematics. This work has been carried out thanks to the collaboration between the Department of Mathematics and the Department of Foreign Languages. Five different typologies of questions are analysed: "Combining sentence elements" asks to combine given elements to form at least three correct and meaningful sentences; "Find the mistake" asks whether a sentence is grammatically correct and to explain why; "The Target puzzle" asks to join words together using precise links; "Scrambled text" asks the student the correct sequence of sentences in a scrambled text; "The Encrypted Crossword puzzle" is a crossword puzzle without definition clues. Linguists consider these question types effective to develop language competences and have already used them profitably in traditional teaching and language learning. To engage the learner and increase motivation, we will develop an implementation where the questions are self-evaluated, always available, with immediate and interactive feedback. These tools could be used in online language courses and in all those teaching activities that are carried out remotely by the student alone. All questions are outlined with the explanation of the grading code and a triplet of descriptors; they describe the Performance, Requirements and Objectives of each question and allow further studies on automatic detection of the relatedness between different language learning objects, in order to construct adaptive language tests.
IoT Database Forensics

The data that are generated by several devices in the IoT realm require careful and real time processing. Recently, researchers have concentrated on the usage of cloud databases for storing such data to improve efficiency. HarperDB aims at producing a DBMS that is relational and non-relational simultaneously, to help journeymen developers creating products and servers in the IoT space. Much of what the HarperDB team has talked about has been achieved, but from a security perspective, a lot of improvements need to be made. The team has clearly focused on the problems that exist from a database and data point of view, creating a structure that is unique, fast, easy to use and has great potential to grow with a startup. The functionality and ease of use of this DBMS is not in question, however as the trade-off triangle to the right suggests, this does entail an impact to security. In this paper, using multiple forensic methodologies, we performed an in-depth forensic analysis on HarperDB and found several areas of extreme concern, such as lack of logging functionalities, basic level of authorisation, exposure of users' access rights to any party using the database, There had to be a focus on preventative advice instead of reactive workarounds due to the nature of the flaws found in HarperDB. As such, we provide a number of recommendations for the users and developers.
MIDAS at SemEval-2019 Task 9: Suggestion Mining from Online Reviews using ULMFit

In this paper we present our approach and the system description for Sub Task A of SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums.Given a sentence, the task asks to predict whether the sentence consists of a suggestion or not.Our model is based on Universal Language Model Finetuning for Text Classification.We apply various pre-processing techniques before training the language and the classification model.We further provide detailed analysis of the results obtained using the trained model.Our team ranked 10th out of 34 participants, achieving an F1 score of 0.7011.We publicly share our implementation 1 .
Multi-Label Product Categorization Using Multi-Modal Fusion Models

In this study, we investigated multi-modal approaches using images, descriptions, and titles to categorize e-commerce products on Amazon. Specifically, we examined late fusion models, where the modalities are fused at the decision level. Products were each assigned multiple labels, and the hierarchy in the labels were flattened and filtered. For our individual baseline models, we modified a CNN architecture to classify the description and title, and then modified Keras' ResNet-50 to classify the images, achieving $F_1$ scores of 77.0%, 82.7%, and 61.0%, respectively. In comparison, our tri-modal late fusion model can classify products more effectively than single modal models can, improving the $F_1$ score to 88.2%. Each modality complemented the shortcomings of the other modalities, demonstrating that increasing the number of modalities can be an effective method for improving the performance of multi-label classification problems.
Yimmon at SemEval-2019 Task 9: Suggestion Mining with Hybrid Augmented Approaches

Suggestion mining task aims to extract tips, advice, and recommendations from unstructured text.The task includes many challenges, such as class imbalance, figurative expressions, context dependency, and long and complex sentences.This paper gives a detailed system description of our submission in SemEval 2019 Task 9 Subtask A. We transfer Self-Attention Network (SAN), a successful model in machine reading comprehension field, into this task.Our model concentrates on modeling long-term dependency which is indispensable to parse long and complex sentences.Besides, we also adopt techniques, such as contextualized embedding, back-translation, and auxiliary loss, to augment the system.Our model achieves a performance of F1=76.3, and rank 4th among 34 participating systems.Further ablation study shows that the techniques used in our system are beneficial to the performance.
ReachNN: Reachability Analysis of Neural-Network Controlled Systems

Applying neural networks as controllers in dynamical systems has shown great promises. However, it is critical yet challenging to verify the safety of such control systems with neural-network controllers in the loop. Previous methods for verifying neural network controlled systems are limited to a few specific activation functions. In this work, we propose a new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions, i.e., as long as they ensure that the neural networks are Lipschitz continuous. Specifically, we consider abstracting feedforward neural networks with Bernstein polynomials for a small subset of inputs. To quantify the error introduced by abstraction, we provide both theoretical error bound estimation based on the theory of Bernstein polynomials and more practical sampling based error bound estimation, following a tight Lipschitz constant estimation approach based on forward reachability analysis. Compared with previous methods, our approach addresses a much broader set of neural networks, including heterogeneous neural networks that contain multiple types of activation functions. Experiment results on a variety of benchmarks show the effectiveness of our approach.
Analisis Korelasi Antara Kebiasaan Membaca Dan Kemampuan Membaca Pemahaman Pada Siswa Kelas IX SMP Kemala Bayangkari Makassar

This study aims to describe whether or not there is a relationship between reading habits and reading comprehension ability in class IX students of SMP Kemala Bayangkari Makassar. This study is a descriptive study using correlational analysis methods. The population in this study was 200 students and the sample was 40 students. In this study data was collected from two sources, namely the questionnaire value from the results of the reading habit questionnaire test and the value of reading comprehension ability from the results of the comprehension reading ability test. Data obtained in the form of primary data using a quantitative descriptive approach with an objective process and analyzing data. The results of the analysis in this study indicate that the relationship between reading habits and reading comprehension ability of class IX students at SMP Kemala Bayangkari Makassar has a positive correlation. In this study, it is hoped that the readers and writers themselves will be able to further improve their reading skills.
Consistency of Outputs of the Selected Motion Acquisition Methods for Human Activity Recognition

The aim of this paper is to choose the optimal motion sensor for the selected human activity recognition. In the described studies, different human motion measurement methods are used simultaneously such as optoelectronics, video, electromyographic, accelerometric, and pressure sensors. Several analyses of activity recognition were performed: recognition correctness for all activities together, matrices of the recognition errors of the individual activities for all volunteers for the individual sensors, and recognition correctness of all activities for each volunteer and each sensor. The experiments enabled to find a range of interchangeability and to choose the most appropriate sensor for recognition of the selected motion.
The Nature of Software Evolution

This chapter considers the nature of software evolution: What kinds of software systems are evolved? Which quality aspects of software systems play a role throughout evolution? What kinds of software changes exist and which evolution processes are considered? What is the impact of these changes? The purpose of this chapter is to clarify the fundamental aspects of software evolution, which are being taken up again in the following chapters. Hence, this chapter shall explain the basic terminology used in this book. To a small extent, it shall also provide a domain analysis of the area of software evolution. And finally, for more details, further scenarios, and examples of the fundamental aspects of software evolution, the reader can find references to follow-up chapters. In this way, this chapter helps to identify how the contributions of subsequent chapters fit into the big picture of software evolution.
The Rise of “Digital Biology”: We need not only open, FAIR but also sustainable data!

Biology has already experienced great divides that decreased its global coherence and its ability to answer important scientific and societal concerns. For example in the XXth century, the so-called “Life Sciences” developed remarkably in comparison to Natural History sciences. This way, the approaches on model organisms dominated or prevented other approaches from being carried out on more diverse organisms, which may have given a misleading feeling of generality for the results obtained. Another great divide is at risk of developing now with the rise of what could be called “Digital Biology,” separating from other “material-based” approaches in its tendency to consider digital data only. Some biologists adopt a somewhat essentialist view of species and DNA, considering that enough knowledge is now accumulated, and that species records can be kept and saved as digital data only (Grandcolas 2017). Examples of this include occurrence records without specimens or auxiliary documents, taxonomic descriptions based on photographs, DNA sequences without vouchers, and, lastly, DNA sequences without taxonomic names. This tendency puts at risk the sustainability, growth, and coherence of biological knowledge that is organized in a system wherein all data and notions are connected via specimens, with names and sequences being a retrieval means (Troudet et al. 2018). This tendency also ignores the robust foundation of biology, the data of which are linked to collections, vouchers, and stocks. The foundation of physical specimens exists for data concerning any live beings, be they rare wild species or selected lines of model organisms. There are now many calls for open and FAIR science, with results, methods, tools, and data not only findable, accessible, and interoperable but also re-usable. More than FAIR and digitally re-usable, data need to be sustainable. It is needed that their meaning and significance can be re-analysed, re-interpreted by going back as far as possible to material vouchers. We urge then scientists to consider this question by providing all necessary material elements to make open and FAIR data sustainable as well.
Integration of educational robotics in Primary Education

As technology advances, its influence in different sectors of our society is increasing. The educational field is not left out and is also forced to reconfigure itself assessing aspects which have not been considered until few years ago, as is the case of the introduction and application of ICT or educational robotics. The aim of this article is to analyze the perception and opinion of teachers, families and students of a school in the Basque Country about educational robotics, programming and related digital skills in the stage of Primary Education. To this end, a course has been carried out in which a group of 2nd grade students from Elementary School. work for the first time with different resources and materials of educational robotics: Lego WeDo 2.0 and Blue-bot. The intention is to contrast the opinion of some members of the educational community before and after the course; for which various instruments will be used. Mention that, according to the general results, there are differences between the pretest and the posttest that benefit the perspective on the integration of educational robotics in the classroom. Likewise, it is concluded that both its pedagogical use and teacher training are controversial issues, although the educational opportunities offered by these technologies are recognized to face the challenges of today’s society.