Reviewerly/exHarmony huggingface dataset:
Efficient Android Phishing Detection Based on Improved Naïve Bayes Algorithm

With the rapid development of the mobile Internet, phishing attacks are becoming more common on mobile phones. In order to effectively detect phishing attacks on Android platforms, this paper proposes an improved framework based on the revised Naive Bayes algorithm. Under this framework, the K-means algorithm is used to supplement missing values of attributes to get the complete datasets. The probability is enlarged to resolve the problem of low biased estimation of the Bayesian algorithm. Weights of different attributes are evaluated to avoid neglecting the relationship among them to improve the accuracy of phishing website detection. The probability ratio of phishing websites to legitimate websites is adjusted to further improve the correct rate of detection. Experimental results have demonstrated that the proposed framework can effectively detect the phishing attacks with relatively small time cost.
Image decomposition using a second-order variational model and wavelet shrinkage

The paper is devoted to the new model for image decomposition, that splits an image $f$ into three components $u+v+\omega$, with $u$ a piecewise-smooth or the ``cartoon'' component, $v$ a texture component and $\omega$ the noise part in variational approach. This decomposition model is in fact incorporates the advantages of two preceding models: the second-order total variation minimization of Rudin-Osher-Fatemi (ROF2), and wavelet shrinkage for oscillatory functions. This decomposition model is presented as an extension of the three components decomposition algorithm of Aujol et al. in \cite{JAC}. It also continues the idea introduced previously by authors in \cite{TPB}, for two components decomposition model. The ROF2 model was first proposed by Bergounioux et al. in \cite{BP}, it is an improved regularization method to overcome the undesirable staircasing effect. The wavelet shrinkage is well combined to separate the oscillating part due to texture from that due to noise. Experimental results validate the proposed algorithm and demonstrate that the image decomposition model presents effective and comparable performance to other state-of-the-art models.
SOCIALIZATION ROLE OF SCHOOL AND HIDDEN CURRICULUM

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 3404-3412ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.0910Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Improving Scientific Article Visibility by Neural Title Simplification.

The rapidly growing amount of data that scientific content providers should deliver to a user makes them create effective recommendation tools. A title of an article is often the only shown element to attract people's attention. We offer an approach to automatic generating titles with various levels of informativeness to benefit from different categories of users. Statistics from ResearchGate used to bias train datasets and specially designed post-processing step applied to neural sequence-to-sequence models allow reaching the desired variety of simplified titles to gain a trade-off between the attractiveness and transparency of recommendation.
FLIPPED E-LEARNING SCENARIO FOR TEACHING MOBILE DEVELOPMENT

1 Graz University of Technology (AUSTRIA)2 Peter the Great Saint-Petersburg Polytechnic University (RUSSIAN FEDERATION)
Suffix Arrays with a Twist

The suffix array is a classic full-text index, combining effectiveness with simplicity. We discuss three approaches aiming to improve its efficiency even more: changes to the navigation, data layout and adding extra data. In short, we show that i) the way how we search for the right interval boundary impacts significantly the overall search speed, ii) a B-tree data layout easily wins over the standard one, iii) the well-known idea of a lookup table for the prefixes of the suffixes can be refined with using compression, iv) caching prefixes of the suffixes in a helper array can pose another practical space-time tradeoff.
Cloud designs and deployment models: a systematic mapping study

Cloud computing is a unique paradigm that is aggregating resources available from cloud service providers for use by customers on demand and pay per use basis. There is a Cloud federation that integrates the four primary Cloud models and the Cloud aggregator that integrates multiple computing services. A systematic mapping study provides an overview of work done in a particular field of interest and identifies gaps for further research. The objective of this paper was to conduct a study of deployment and designs models for Cloud using a systematic mapping process. The methodology involves examining core aspect of the field of study using the research, contribution and topic facets. The results obtained indicated that there were more publications on solution proposals, which constituted 41.98% of papers relating to design and deployment models on the Cloud. Out of this, 5.34% was on security, 1.5% on privacy, 6.11% on configuration, 7.63% on implementation, 11.45% on service deployment, and 9.92% of the solution proposal was on design. The results obtained will be useful for further studies by the academia and industry in this broad topic that was examined.
ADOPTION FACTORS OF HEALTH MONITORINGSYSTEMS FOR SMART HEALTHCARE: A SYSTEMATICREVIEW

Although there have been several studies that focus on technical aspects of health monitoring systems, adoption of these systems remains largely unknown.A limited number of researches has explored the factors influencing users' adoption of these systems that provide successful system implementation.In this study, we reviewed articles addresses the adoption of health monitoring systems.We extract criteria that influence the adoption decision that has been made at some stage to use health monitoring systems.Then group these criteria into nine categories in terms of cost, ease of use, environment, knowledge, management, security, social influence, subjective norm, technical, usefulness, and enjoyment.By using these criteria, we develop an adoption model for health monitoring systems.
Alexa, Open Analyst Assistant”: Evaluating a Voice User Interface for Financial Analysis

Tech-savvy creators have developed countless voice interaction applications for personal and social use, but there is a limited production of voice applications for financial and investment analysis use cases.We show how a voice user interface (VUI) can impact the workflow of financial analysts.Specifically, we hypothesized that a VUI could lead to improvements on financial analysts' job productivity, job performance, and job effectiveness.We ran a controlled experiment where we randomly assigned thirty-two financial analysts to complete six tasks with their current workflow tools, with VUI through the publicly available Alexa skills, or with VUI using an internally developed Alexa skill for financial analysts.Analysts required less time, fewer steps and were able to multi-task for completion of tasks with the use of VUI.We suggest that VUI in the workplace could improve the productivity and ease of financial analysts' workflow.
DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level

Every year millions of men, women and children are forced to leave their homes and seek refuge from wars, human rights violations, persecution, and natural disasters. The number of forcibly displaced people came at a record rate of 44,400 every day throughout 2017, raising the cumulative total to 68.5 million at the years end, overtaken the total population of the United Kingdom. Up to 85% of the forcibly displaced find refuge in low- and middle-income countries, calling for increased humanitarian assistance worldwide. To reduce the amount of manual labour required for human-rights-related image analysis, we introduce DisplaceNet, a novel model which infers potential displaced people from images by integrating the control level of the situation and conventional convolutional neural network (CNN) classifier into one framework for image classification. Experimental results show that DisplaceNet achieves up to 4% coverage-the proportion of a data set for which a classifier is able to produce a prediction-gain over the sole use of a CNN classifier. Our dataset, codes and trained models will be available online at https://github.com/GKalliatakis/DisplaceNet.
Exploring the relationships between executive management, success of information systems and performance in educational organizations

This article presents the preliminary results of a study that has as objective to establish if the executive management affects the success of the information systems used in the management processes, and the latter affects the organizational performance of educational organizations of primary and secondary education. For this purpose, surveys were applied to key informants who use and / or know the information systems available to support the management of their institutions in Chile. The results obtained indicate that factors associated with executive management affect the success of information systems, but the effect of this success on their performance is low.
Business Inefficiencies and Youth Unemployment in Ethiopia: A Case Study of Mettu Town

This paper summarizes the arguments and counterarguments within the scientific discussion on the issues of unemployment in Ethiopia.The main purpose of the research is to examine the trend and determinants of unemployment in context of the inefficient manner of business in Ethiopia particularly in Mettu Town.The other specific objectives are to examine the current situation and trends of unemployment, to examine socioeconomic characteristics of unemployment and to identify the major factors that are responsible for unemployment in Mettu town of Ethiopia.Systematization of literary sources and approaches for solving the problem has widely been used that indicates that there is a contradictory scalier of unemployment in Ethiopia.The relevance of the decision of this scientific problem is how population growth, urbanization, and higher education increase unemployment in the country during the adoption of new business policy.Investigation of the unemployment topic in Ethiopia in the paper is carried out in the following logical sequence measuring the population growth; urbanization and education, which have an impact on unemployment.Methodological tools of the research methods were comparative analysis using statistical tools and techniques used in years of research 2018-2019.The object of the research are the regions specified systematically because namely they were elaborated.The paper presents the results of an empirical analysis of the unemployment trend and its determinant, which showed that there is a contradictory paradox among parameters.The research empirically confirms and theoretically proves that unemployment, particularly among youth, is creeping ahead in the region because of population growth, rapid urbanization and business inefficiencies as well.The results of the research can be useful for policymakers, academicians and researchers.
Mixed Discrete Continuous Non-Linear Planning through Piecewise Linear Approximation

Reasoning with continuously changing numeric quantities is vital to applying planners in many real-world scenarios. Several planners capable of doing this have been developed recently. Scalability remains a challenge for such planners, especially those that reason with non-linear continuous change. In this paper, we present a novel approach to reasoning with non-linear domains. Bounding the problem using linear over and under-estimators, allows us to use scalable planners that handle linear change to find plans for non-linear domains. We compare the performance of our approach to existing planners on several domains and demonstrate that our planner can achieve state-of-the-art performance in non-linear planning.
Automatic Verification of FSA Strategies via Counterexample-Guided Local Search for Invariants

Strategy representation and reasoning has received much attention over the past years. In this paper, we consider the representation of general strategies that solve a class of (possibly infinitely many) games with similar structures, and their automatic verification, which is an undecidable problem. We propose to represent a general strategy by an FSA (Finite State Automaton) with edges labelled by restricted Golog programs. We formalize the semantics of FSA strategies in the situation calculus. Then we propose an incomplete method for verifying whether an FSA strategy is a winning strategy by counterexample-guided local search for appropriate invariants. We implemented our method and did experiments on combinatorial game and also single-agent domains. Experimental results showed that our system can successfully verify most of them within a reasonable amount of time.
Relationship between Test Item Arrangements and Testee's Performance and Test Usefulness Criteria

Test method, test content and test type are supposed to affect test taker's performance and have close connectionswith test characteristics. However, these issues have not been subject to much in-depth investigations. To shed somelights on the issues like test item arrangements in relation to test taker's performance, test usefulness criteriaincluding test Validity, Reliability, Impact, Interactiveness, Authenticity, and Practicality, and also test characteristicssuch as Item Facility (IF), Item Discrimination (ID), and Choice Distribution (CD), this ex post facto design studywas conducted with a group of Iranian EFL learners. For this purpose, some university randomly selected studentsmajoring in English Language Teaching, English Translation Studies, and English Literature and learners fromdifferent language institutes received a version of the Nelson proficiency test. Then, two different versions of a sameresearcher-made test in terms of item arrangements were administered to 116 students obtaining an acceptable scorein the Nelson proficiency test. Simultaneously, a Test Usefulness Criteria Questionnaire developed and validated byAbbasian and Nassirian (2015) based on Bachman and Palmer’s (1996) framework, was also attempted by theparticipants. Respective statistical analyses revealed contradictorily that test item arrangement did not have anysignificant effect on the performance of the test takers. However, test usefulness criteria, though with varying extent,proved to be subject to test item arrangement. In the same vein, IF and ID were also affected in the light of testmethod facet. The first contradictory finding leaves room open for further research, while the reaming findings offerinsights to test developers, classroom teachers and all practitioners to pay due attention to test method facets in theireducational assessment decisions as the learners' performance, nature of the measurement devices as well as thenature of the construct itself are all affected test method factors.
Stochastic Approximation Proximal Method of Multipliers for Convex Stochastic Programming

This paper considers the problem of minimizing a convex expectation function over a closed convex set, coupled with a set of inequality convex expectation constraints. We present a new stochastic approximation proximal method of multipliers to solve this convex stochastic optimization problem. We analyze regrets of the proposed method for solving convex stochastic optimization problems. Under mild conditions, we show that this method exhibits sublinear regret for both objective reduction and constraint violation if parameters in the algorithm are properly chosen. Moreover, we investigate the high probability performance of the proposed method under the standard light-tail assumption. Funding: This work was supported by Fundamental Research Funds for the Central Universities [Grant DUT20LK33]; Dalian High-Level Talent Innovation Program [Grant 2020RD09]; National Natural Science Foundation of China [Grant 11731013, 11871135, 11971089, and 12071055].
Modified Local Binary Pattern Algorithm for Feature Dimensionality Reduction

Background: Bio metric authentication is becoming popular now a days and becoming integral part of IoT and other systems. Face recognition is one of the major and important aspect of bio metric systems after the fingerprint. Objective: A face recognition algorithm with feature dimensionality reduction is proposed which is very much required in recognition system for high speed and accuracy. Methods: The proposed algorithm is based on a variant of Local Binary Pattern (LBP) for face detection and recognition. The features of each block of face image is extracted and then global feature of face is constructed from super histogram. Results: For recognition, traditional methods are used. The query image is compared with the data set (ORL Dataset, LFW Dataset and Yale Dataset) in similarity index and the minimum distance. The maximum similarity is used to define as the class of query image. The reduction in number of features is achieved by modifying the traditional LBP process. Conclusion: The proposed modified method is observed as more fast and efficient for face recognition as compared to the existing algorithms.
Landmark Selection for Zero-shot Learning

Zero-shot learning (ZSL) is an emerging research topic whose goal is to build recognition models for previously unseen classes. The basic idea of ZSL is based on heterogeneous feature matching which learns a compatibility function between image and class features using seen classes. The function is constructed based on one-vs-all training in which each class has only one class feature and many image features. Existing ZSL works mostly treat all image features equivalently. However, in this paper we argue that it is more reasonable to use some representative cross-domain data instead of all. Motivated by this idea, we propose a novel approach, termed as Landmark Selection(LAST) for ZSL. LAST is able to identify representative cross-domain features which further lead to better image-class compatibility function. Experiments on several ZSL datasets including ImageNet demonstrate the superiority of LAST to the state-of-the-arts.
Adaptive Planning with Evidence Based Prediction for Improved Fluency in Routine Human-Robot Collaborative Tasks

This thesis work intends to explore the development of a shared mental model between an autonomous agent and a human, where we aim to promote fluency in continuing interactions defined by repetitive tasks. That is, with repetitive actions, experimentation and increasing iterations, we wish the robot to learn how its own behavior affects that of its partner. To accomplish this, we propose a model that encodes both human and robot actions in a probabilistic space describing the temporal transition points between activities. The purpose of such a model is not only in passive predictive power (understanding the future actions of an associate), but also to encode the latent effect of a robot’s action on the future actions of the associate.
FiST – towards a free Semantic Tagger of modern standard Finnish

This paper introduces a work in progress for implementing a free full text semantic tagger for Finnish, FiST.The tagger is based on a 46 226 lexeme semantic lexicon of Finnish that was published in 2016.The basis of the semantic lexicon was developed in the early 2000s in an EU funded project Benedict (Löfberg et al., 2005).Löfberg (2017) describes compilation of the lexicon and evaluates a proprietary version of the Finnish Semantic Tagger, the FST 2 .The FST and its lexicon were developed using the English Semantic Tagger (The EST) of University of Lancaster as a model.This semantic tagger was developed at the University Centre for Corpus Research on Language (UCREL) at Lancaster University as part of the UCREL Semantic Analysis System (USAS 3 ) framework.The semantic lexicon of the USAS framework is based on the modified and enriched categories of the Longman Lexicon of Contemporary English (McArthur, 1981).We have implemented a basic working version of a new full text semantic tagger for Finnish based on freely available components.The implementation uses Omorfi and FinnPos for morphological analysis of Finnish words.After the morphological recognition phase words from the 46K semantic lexicon are matched against the morphologically unambiguous base forms.In our comprehensive tests the lexical tagging coverage of the current implementation is around 82-90% with different text types.The present version needs still some enhancements, at least processing of semantic ambiguity of words and analysis of compounds, and perhaps also treatment of multiword expressions.Also a semantically marked ground truth evaluation collection should be established for evaluation of the tagger. TiivistelmäSuomessa on harjoitettu kieliteknologiaa laaja-alaisesti 1980-luvun alusta, ja melkein 40 vuotta jatkunut tutkimus ja kehitystyö on tuottanut useita merkittäviä ohjelmistoja suomen kielen analyysiin.Alkuvuosikymmenien käytöltään rajoitetuista ohjelmistoista on siirrytty 2000-luvulla paljolti joko avoimen lähdekoodin ohjelmiin tai ohjelmien vapaaseen saatavuuteen.Vapaasti saatavia suomen kielen keskeisiä kieliteknologisia ohjelmia on olemassa tällä hetkellä hyvin morfologiseen ja syntaktiseen analyysiin, esimerkiksi Omorfi, Voikko ja FinnPos morfologiaan ja Finnish depedency parser lauseenjäsennykseen.FiNER-ohjelmistolla voidaan tunnistaa
Exploring data sampling techniques for imbalanced classification problems

The class imbalance problem is one of the key challenges in machine learning and data mining. Imbalanced data can result in the sub-optimal performance of classification models. To address the problem, a variety of data sampling methods have been proposed in previous studies. However, there is no universal solution and it is worth to explore which kind of data sampling technique is more effective in balancing class distribution in terms of the type of data and classifier. In this work, we present an experimental study based on a number of real-world data sets obtained from different disciplines. The goal is to investigate different sampling techniques in terms of the effectiveness of increasing the classification performance in imbalanced data sets. In particular, we study ten sampling methods of different types, including random sampling, clusterbased sampling, ensemble sampling and so on. Besides, the C4.5 decision tree algorithm is used to train the base classifiers and the performance is measured by using precision, G-Measure and Cohen's Kappa statistic.
SIGCSE mailing lists

For me, one of the best SIGCSE membership benefits is having access to the organization's mailing lists. We first began using an electronic listserv or mailing list in 1994 to be able to communicate with each other more easily and discuss issues and concerns beyond the conversations at conferences. The mailing list allowed us to notify members of upcoming events, deadlines, and other important notices. Did you know that in 2010 we actually had seven active mailing lists? Before the days of Google groups, Dropbox, and cloud computing, all active committees and subgroups had a mailing list for member communication. Slowly those were phased out as those groups wrapped up their work.