Reviewerly/exHarmony huggingface dataset:
Information Security in Agile Software Development Projects: A Critical Success Factor Perspective

© 27th European Conference on Information Systems - Information Systems for a Sharing Society, ECIS 2019. All rights reserved. The importance of information security in software development projects is long recognised, with many comprehensive standards and procedures in use to provide assurance of information security. The agile development paradigm conflicts with traditional security assurance by emphasising the delivery of functional requirements and a reduction in structured and linear development styles. Through a series of thirteen qualitative interviews, this study identifies practices that address this problem which have been successfully adopted by agile practitioners. The findings present four categories of practices - organisational, team, project, and technical - and twelve critical success factors that should be explicitly considered by practitioners to assure agile security. The critical success factors provide a foundation for practitioners to strategically identify and develop best practices to embed information security in agile development projects. The identified categories also highlight the importance of agile security practices centring around individuals and culture and contributes to the literature by providing a representation of agile security practices that encompasses a broad range of focal areas.
Artificial Intelligence to Design a Mask Insensible to the Distance From the Camera to the Scene Objects

The sharpness of an image depends on the spatial frequency response of the photographic imaging system and the sensor characteristics. In conventional digital cameras, only those objects within a distance range are in focus, while other objects are captured with different amounts of blurring depending on their distance to the focal plane. This can be desired for some applications; however, this can also be undesired because some objects in the scene may be blurred and impossible to recover. In the field of augmented reality, simulating this natural effect of showing sharp objects in combination with blurred objects increases the visual realism of augmented video. In order to simulate this effect, it is very important to capture all objects in the scene with high quality so that it could be possible to dynamically blur different objects in the scene at runtime. In this paper, we present an algorithm to find a set of possible complex-amplitude transmittance masks capable of considerably reducing the impact of focus errors in the scene objects. Computer simulations are used to compare the masks found in this paper with a classic mask in the state of the art. The main contribution of this paper is the use of Chebyshev polynomials to model an optical mask, and then, use artificial intelligence to establish some properties of this mask, such as the depth of field, the resolution, and the amount of gathered light.
Do concern mining tools really help requirements analysts? An empirical study of the vetting process

Software requirements are often described in natural language because they are useful to communicate and validate. Due to their focus on particular facets of a system, this kind of specifications tends to keep relevant concerns (also known as early aspects) from the analysts’ view. These concerns are known as crosscutting concerns because they appear scattered among documents. Concern mining tools can help analysts to uncover concerns latent in the text and bring them to their attention. Nonetheless, analysts are responsible for vetting tool-generated solutions, because the detection of concerns is currently far from perfect. In this article, we empirically investigate the role of analysts in the concern vetting process, which has been little studied in the literature. In particular, we report on the behavior and performance of 55 subjects in three case-studies working with solutions produced by two different tools, assessed in terms of binary classification measures. We discovered that analysts can improve “bad” solutions to a great extent, but performed significantly better with “good” solutions. We also noticed that the vetting time is not a decisive factor to their final accuracy. Finally, we observed that subjects working with solutions substantially different from those of existing tools (better recall) can also achieve a good performance.
I WANT A LAMBORGHINI: AN ETHNOGRAPHY OF CRYPTOCURRENCY COMMUNITIES

Understanding how digital technologies transform service businesses more generally, and financial services more specifically, is an ongoing research challenge for the Information Systems (IS) discipline. In an effort to gain insight to this problem, we undertook an in-depth ethnographic study of several hundred cryptocurrencies miners, traders and developers. We present a typology of those involved in this cryptocurrency ecosystem, identifying actors as one of 'Knowledge-Seeker', 'Visionary', 'Novice', or 'Fortune Hunter'. This typology exposes the roles individuals in cryptocurrency communities play in transforming financial services by delineating the actions of cryptocurrency service providers, blockchain educators and cryptocurrency communities. By exploring how the roles of actors changed over 8 months, we highlight that learning is a precondition for value cocreation and transformation in financial services, and offer a theoretical and contextual contribution by extending digital transformation insights from customer perspectives. Furthermore, our empirical results contribute to a better understanding of how new customer roles emerge and transform, which we presented as two distinct customer role trajectories in cryptocurrency-enabled service ecosystems.
Forensic analysis of characteristic features of disguised writing in fixing authorship

Forensic analysis of characteristic features of disguised writing in fixing authorship - IJFMTS- Print ISSN No: - 2581-9844 Online ISSN No:- 2456-9615 Article DOI No:- 10.18231/j.ijfmts.2019.006, IP International Journal of Forensic Medicine and Toxicological Sciences-IP Int J Forensic Med Toxicol Sci
Solving the Problem of Coordination and Control of Multiple UAVs by Using the Column Generation Method

In this paper, we consider the problem of autonomous task allocation and trajectory planning for a set of UAVs. This is a bi-level problem: the upper-level is a task assignment problem, subjected to UAV capability constraints; the lower-level constructs the detailed trajectory of UAVs, subjected to dynamics, avoidance and dependency constraints. Although the entire problem can be formulated as a mixed-integer linear program (MILP), and thus it can be solved by available software, the computational time increases intensively. For solving more efficiently this problem we propose an efficient approach based on the column generation method in which the modified dependency constraint will be added into the sub-problem. The performance of our approach is evaluated by comparing with solution given by the CPLEX on different scenarios.
Sistem Pakar Berbasis Web Menggunakan Metode Forward Chaining Untuk Mendiagnosa Penyakit Pulpa Dan Periapikal

Pulp and periacal disease is happening in Indonesia. This is because knowledge and people awarness to take care their dental health is in low level, so that a few of people in Indonesia do not know types, symptoms, and the solution, how to resolve the problem. Altough pulp and periacal disease have a bad impact like the patient's quality of life. Besides the cost of expensive dental care and have an impact on a person's overall health, such as can cause catastrophic diseases such as stroke, heart desease, and kidney. Based on these conditions, very important to make a web-based expert system to diagnose this problem with forward chaining methode. The expert system is created using Dreamweaver C56, PHP, CSS, and MySQL. Through this system application, people will find out the information and get consultations about pulp and periapical disease easily and quickly, so that it can be dealt with quickly and correctly.
RRLUFF: Ranking function based on Reinforcement Learning using User Feedback and Web Document Features

Principal aim of a search engine is to provide the sorted results according to user’s requirements. To achieve this aim, it employs ranking methods to rank the web documents based on their significance and relevance to user query. The novelty of this paper is to provide user feedback-based ranking algorithm using reinforcement learning. The proposed algorithm is called RRLUFF, in which the ranking system is considered as the agent of the learning system and the selection of documents is displayed to the user as the agent's action. Reinforcement signal in this system is calculated based on user's click on the documents. Action-values in the RRLUFF algorithm are calculated for each feature of the document-query pair. In RRLUFF method, each feature is scored based on the number of the documents related to the query and their position in the ranked list of that feature. For learning, documents are sorted according to modified scores for the next query. Then, according to the position of a document in the ranking list, some documents are selected based on the random distribution of their scores to display to the user. OHSUMED and DOTIR benchmark datasets are used to evaluate the proposed method. The evaluation results indicate that the proposed method is more effective than the related methods in terms of P@n, NDCG@n, MAP, and NWN.
Design Variations of Mount Olympus’ Relief in a Virtual Reality Environment

The purpose of this article is to describe the procedures and practices that were followed during the early stage of a research entitled “Use of game engines in the design and implementation of Mount Olympus in a virtual reality environment” - funded through a scholarship by the Stavros Niarchos Foundation - and to present the results of the design process that was followed. The main aim of this stage was an attempt to approach the modern methodologies of representation and production of virtual interactive landscapes as well as to familiarize with the use of the available software for the creation of interactive representations that can be integrated into a narrative digital geo-environment.
High Performance Algorithms for Counting Collisions and Pairwise Interactions

The problem of counting collisions or interactions is common in areas as computer graphics and scientific simulations. Since it is a major bottleneck in applications of these areas, a lot of research has been carried out on such subject, mainly focused on techniques that allow calculations to be performed within pruned sets of objects. This paper focuses on how interaction calculation (such as collisions) within these sets can be done more efficiently than existing approaches. Two algorithms are proposed: a sequential algorithm that has linear complexity at the cost of high memory usage; and a parallel algorithm, mathematically proved to be correct, that manages to use GPU resources more efficiently than existing approaches. The proposed and existing algorithms were implemented, and experiments show a speedup of 21.7 for the sequential algorithm (on small problem size), and 1.12 for the parallel proposal (large problem size). By improving interaction calculation, this work contributes to research areas that promote interconnection in the modern world, such as computer graphics and robotics.
Scanpath components reveal how eye movement reinstatements differentially contribute to episodic remembering

An extensive body of research has shown that episodic remembering involves spontaneous eye movements that largely reproduce the gaze patterns that were present during encoding (e.g., Brandt & Stark, 1997; Johansson et al., 2012). Recent work has further shown that fixation locations that overlap between encoding and recall promote cortical episodic reconstruction (Bone et al., 2018; Johansson et al., 2018) and successful remembering (Johansson & Johansson, 2014). While such findings indicate that gaze location plays an active role during episodic reconstruction, the unfolding scanpaths also encompass more complex information over and above simple gaze locations, such as order, direction, shape, length and duration. Virtually nothing is known about how such spatio-temporal components contribute to episodic reconstruction. The present study investigated the encoding-retrieval overlap in scanpaths for 60 participants who encoded and recalled 36 visuospatial stimuli of two types: scenes and object arrangements. Results replicate and extend previous findings, by analyzing scanpath reinstatement over a multitude of spatio-temporal components. Critically, by combining subjective ratings of memory quality with a surprise test of forced-choice recognition, we demonstrate how such components contribute to successful remembering to different extents, and in different ways depending on the stimulus type. Results indicate that scanpath shape contributes to reconstructing the global scene structure whereas scanpath position, order and direction contribute to reconstructing the arrangement of individual objects in a spatial context. To our knowledge, this is the first systematic demonstration of how eye movement reinstatements contribute to episodic remembering in a multifaceted way.
Synthetic Classification and Diverse Communities

This brief paper argues that a synthetic approach to classification can alleviate all of the major concerns that are commonly raised about how Knowledge Organization Systems (KOSs) may disserve various communities. It surveys how a synthetic approach can potentially address a variety of concerns regarding KOSs and social diversity.
A Generic Framework for Higher-Order Generalizations.

We consider a generic framework for anti-unification of simply typed lambda terms. It helps to compute generalizations which contain maximally common top part of the input expressions, without nesting generalization variables. The rules of the corresponding anti-unification algorithm are formulated, and their soundness and termination are proved. The algorithm depends on a parameter which decides how to choose terms under generalization variables. Changing the particular values of the parameter, we obtained four new unitary variants of higher-order anti-unification and also showed how the already known pattern generalization fits into the schema.
Bot2Vec: Learning Representations of Chatbots

Chatbots (i.e., bots) are becoming widely used in multiple domains, along with supporting bot programming platforms.These platforms are equipped with novel testing tools aimed at improving the quality of individual chatbots.Doing so requires an understanding of what sort of bots are being built (captured by their underlying conversation graphs) and how well they perform (derived through analysis of conversation logs).In this paper, we propose a new model, BOT2VEC, that embeds bots to a compact representation based on their structure and usage logs.Then, we utilize BOT2VEC representations to improve the quality of two bot analysis tasks.Using conversation data and graphs of over than 90 bots, we show that BOT2VEC representations improve detection performance by more than 16% for both tasks.Agent: Hi, I'm Eva a virtual assistant, how may I help you?Customer: I'm having some
Interaction and Autonomy in Foreign Language and Culture Studies

The article focuses on the problem of developing interactive and autonomous skills of the future teachers of foreign language and culture within the university educational programme. The study evaluates the effectiveness of interactive activities in promoting learners’ autonomy of pre-service teachers in the course of English language and culture studies at university and provides teaching tips for implementing the interactive model in language teachers’ education. The research adopted a pretest-posttest quasi-experimental method design and the effectiveness of interactive language classroom model was measured against a traditionally taught class with the help of self-evaluation charts based on a Likert-type scale. The findings of the experimental teaching revealed a higher level of self-beliefs of respondents of the experimental group about the development of their autonomous skills and professional confidence in their education in foreign language and culture, which indicated the effectiveness of interactive activities in developing learners’ autonomy and a correlation between interaction and autonomy in linguistic and cultural studies. It was concluded in the experimental study that autonomy as a personal characteristic can be developed through interactive practice in and outside the classroom, and the interactive model of teaching can be successfully integrated into the concept of autonomous language and culture studies.
Foreword to the Special Section on Cyberworlds 2018

• Sketching and sculpting as interaction techniques in sketch-based modeling. • Virtual reality and perceived color temperature through user studies. • LifeBrush, an interactive simulation and visualization system of mitochondria within VR. • User Study of speech vs 2D and 3D user interfaces in immersive VR.
The social and cultural approach to forming geometric concepts among schoolchildren

In line with the social and cultural approach, the problem of forming mathematical concepts among schoolchildren as the system of judgments is considered. Concepts at the verbal and logical level are formed through teaching to prove theorems and solve proof problems. Objective: To provide elements of the method of forming geometric concepts by means of specially organized learning activities of schoolchildren to develop the generalized ability to prove with the access to the value-oriented learning. Methods: Theoretical provisions on the laws of the concept formation process are developed. During the experiment forming stage the methodology to teach geometry to pupils of the 7th grade of secondary education institutions is developed. The basis is the activity to develop skills to prove. Relying on instrument-oriented, subject-oriented and value-oriented types of learning is assumed. Findings: During the quantitative and qualitative evaluation of results, the following independent characteristics have been taken into account: the form of action, the level of generalization, the level of expansion, the level of mastering, and the value relation. The results of analyzing the statistical data hav e confirmed the hypothesis about the significant influence of the following factors on the success of mastering geometric concepts by pupils: the mathematical training (significance level 0,003), the effect of electronic educational environment in combination with the evaluated method (significance level 0,001), the duration of training using the evaluated method (significance level 0,01). Conclusions: The hypothesis is substantiated and proved in practice: the process of forming concepts is regularly ensured by the following psychodidactic conditions: the formation of main components of conceptual psychic structures; the formation of declarative, procedural and evaluative knowledge; the gradual formation of the subjective image of the concept content; the gradual development of the entire psychic structure; the gradual development of the activity component of geometric concepts.
Real Time Anomaly Detection in Massive Data Streams with ELK Stack

Real time anomaly detection is very popular topic nowadays this because the number of data generated every day is larger and larger.Facing with the phenomena of Big Data is not an easy task.The main aim of this research is to fine appropriate architecture for real-time big data analytic and its main task is to detect anomalies in this real-time data.In this paper we show the implementation of anomaly detection algorithm in real time infrastructure in order to find anomalies as soon as possible.We have proposed architecture for real time anomaly detection by adding some new components and the main part of the infrastructure is Timelion which enable implementation of different algorithms for anomaly detection.The research is focused to develop infrastructure to monitor ednevnik (education national system in Macedonia) application server and to detect errors in order to scale up the performance.
Doris Martin at SemEval-2019 Task 4: Hyperpartisan News Detection with Generic Semi-supervised Features

In this paper we describe our participation to the Hyperpartisan News Detection shared task at SemEval 2019.Motivated by the late arrival of Doris Martin, we test a previously developed document classification system which consists of a combination of clustering features implemented on top of some simple shallow local features.We show how leveraging distributional features obtained from large in-domain unlabeled data helps to easily and quickly develop a reasonably good performing system for detecting hyperpartisan news.The system and models generated for this task are publicly available.
K. Healy: Data Visualization: A Practical Introduction [Book Review]

This book offers a hands-on master class in building effective data graphics using the ggplot package for the R language. While the implied audience is researchers in the social sciences, there is a lot of practical wisdom here for anyone who works with numerical data. This book treats both the theoretical principles of effective data visualization design (in the mode of Edward Tufte, Stephen Few, Alberto Cairo, and others) as well as concrete guidance on how to integrate such wisdom into a slick data-analysis workflow in the R ecosystem. This practitioner-oriented approach is a very welcomed addition to the literature. By covering both the whys and the hows of data visualization, this single volume swiftly equips researchers to build compelling graphics from their numerical data. The book itself is attractively typeset. One commendable feature is the direct integration of many graphics alongside the corresponding passage of text, by setting them in the wide scholar’s margins. This richly graphical approach makes the lessons engaging, tangible, and enjoyable to read. The book ends with a generous Appendix, which is a compendium of various productivity-oriented tips for working in the R and tidyverse ecosystems. This single volume represents an excellent entry point for those wishing to enhance their capabilities in data visualization. Much of the relevant theoretical work is covered explicitly, and the rest is clearly signposted. The software tools presented are mainstream, open source, and powerful, and the author’s enthusiasm for the ecosystem is infectious. There is only one topic that felt slightly overlooked: the importance of choosing appropriate ranges and divisions for the axes of a graph. Indeed, many of the figures in the book seemed a bit too cluttered with excessive gridlines and tick marks. These are minor gripes, though. Overall, the book is a welcome and timely volume that is full of practical wisdom for producing attractive and effective technical graphics.
Expert-sourcing Domain-specific Knowledge: The Case of Synonym Validation.

One prerequisite for supervised machine learning is high quality labelled data. Acquiring such data is, particularly if expert knowledge is required, costly or even impossible if the task needs to ...
Multi-agent Complex System for Identification of Characteristics and Personality Types and Their Relationship in the Process of Motivation of Students

This paper presents a proposal design of multi-agent complex system for the identification of the characteristics and the types of personality of university students as well as their relationship in the process of motivation for them. Diagnosis and identification of personality types are based on the analysis of knowledge base and the collected information of students with concrete actions that agents through their communication skills and interaction with the rules and standards defined receive, analyze, and determine the identification of outstanding personality type and the most important motivating factors according to the identified personality.