Reviewerly/exHarmony huggingface dataset:
SIEVE: Helping developers sift wheat from chaff via cross-platform analysis

Software developers have benefited from various sources of knowledge such as forums, question-and-answer sites, and social media platforms to help them in various tasks. Extracting software-related knowledge from different platforms involves many challenges. In this paper, we propose an approach to improve the effectiveness of knowledge extraction tasks by performing cross-platform analysis. Our approach is based on transfer representation learning and word embedding, leveraging information extracted from a source platform which contains rich domain-related content. The information extracted is then used to solve tasks in another platform (considered as target platform) with less domain-related content. We first build a word embedding model as a representation learned from the source platform, and use the model to improve the performance of knowledge extraction tasks in the target platform. We experiment with Software Engineering Stack Exchange and Stack Overflow as source platforms, and two different target platforms, i.e., Twitter and YouTube. Our experiments show that our approach improves performance of existing work for the tasks of identifying software-related tweets and helpful YouTube comments.
Evaluation of Couchbase, CouchDB and MongoDB using OSSpal

Document stores are a NoSQL (Not Only Structured Query Language) database type, made to deal with big amounts of data and considered to be very developer-friendly, but they have also attracted a large interest from researchers and practitioners. In this paper, we analyze and evaluate three of the most popular document stores Couchbase, CouchDB, and MongoDB, the evaluation is based on their functionalities. In this evaluation, we use the OSSpal methodology, which combines quantitative and qualitative measures to evaluate the software. We conclude that of these open-source document stores the best is MongoDB, followed by Couchbase, and CouchDB.
Automated Analysis of Job Requirements for Computer Scientists in Online Job Advertisements

The paper presents a concept and a system for the automatic identification of skills in German-language job advertisements. The identification process is divided into Data Acquisition, Language Detection, Section Classification and Skill Recognition. Online job exchanges served as the data source. For identification of the part of a job advertisement containing the requirements, different machine-learning approaches were compared. Skills were extracted based on a POS-template. For classification of the found skills into predefined skill classes, different similarity measures were compared. The identification of the part of a job advertisement containing the requirements works with the pre-trained LinearSVC model for 100% of the tested job advertisements. Extracting skills is difficult because skills can be written in different ways in the German language – especially since the language allows ad-hoc creation of compound. For extraction of skills, POS templates were used. This approach worked for 87.33% of the skills. The combination of a fasttext model and Levenshtein distance achieved a correct assignment of skills to skill classes for 75.33% of the recognized skills. The results show that extracting required skills from German-language job ads is complex.
Things You Might Not Know about the k-Nearest Neighbors Algorithm

Recommender Systems aim at suggesting potentially interesting items to a user. The most common kind of Recommender Systems is Collaborative Filtering which follows an intuition that users who liked the same things in the past, are more likely to be interested in the same things in the future. One of Collaborative Filtering methods is the k Nearest Neighbors algorithm which finds k users who are the most similar to an active user and then it computes recommendations based on the subset of users. The main aim of this paper is to compare two implementations of k Nearest Neighbors algorithm, i.e. from Mahout and LensKit libraries, as well as six similarity measures. We investigate how implementation differences between libraries influence optimal neighborhood size k and prediction error. We also show that measures like F1-score and nDCG are not always a good choice for choosing the best neighborhood size k. Finally, we compare different similarity measures according to the average time of generating recommendations and the prediction error.
Digital Technology Implementation for Smart City and Smart Port Cooperation

A promising direction for the development of society, especially in the conditions of the industry 4.0 paradigm, a of is the Smart City.The purpose of the Smart City is to optimize resources, monitor security, and maximize the level of services for the population.For the successful development of the concept of the Smart City, the integration of various systems and technologies is required.One part of the Smart City concept in particular to consider is the Smart Port.This article examines the existing digital solutions for the automation of Smart Ports, the direction of technology development, the existing problems and prospects for the digitalization of port terminals and transport logistics operators.
Universal Design for Learning in Augmented and Virtual Reality Trainings

This chapter discusses the interrelationship of augmented and virtual reality and Universal Design for Learning (UDL). It provides the results of a structured literature review, proving that there is still very little research on UDL in AR/VR trainings. Furthermore, an exemplary AR/VR learning scenario for craftsmen is presented, developed as part of the German research project ARSuL (Augmented Reality Based Support for Learning in the HVAC industry). The chapter reflects selected design elements behind first AR and VR prototypes of the project through the lens of UDL. Weaknesses are identified and opportunities for improvement derived from UDL principles, guidelines, and checklists.
A parallel domain decomposition algorithm for large scale image denoising

Total variation denoising (TVD) is an effective technique for image denoising, in particular, for recovering blocky, discontinuous images from noisy background. The problem is formulated as an optimization problem in the space of bounded variation functions, and the solution is obtained by solving the associated Euler–Lagrange equation defined on the domain occupied by the entire image. The method offers high quality results, but is computationally expensive for large images, especially for three-dimensional problems. In this paper, we introduce a highly parallel version of the algorithm which formulates the problem as multiple overlapping, but independent, optimization problems, and each is defined on a portion of the image domain. This approach is similar to the overlapping Schwarz type domain decomposition method, but is non-iterative, for solving partial differential equations, and is highly scalable, without using any coarse grids, for parallel computers with a large number of processors. We show by a theory and also by some two- and three-dimensional numerical experiments that the new approach has similar numerical accuracy as the classical TVD approach, but is much more efficient on parallel computers.
On the Difficulty of Warm-Starting Neural Network Training

In many real-world deployments of machine learning systems, data arrive piecemeal. These learning scenarios may be passive, where data arrive incrementally due to structural properties of the problem (e.g., daily financial data) or active, where samples are selected according to a measure of their quality (e.g., experimental design). In both of these cases, we are building a sequence of models that incorporate an increasing amount of data. We would like each of these models in the sequence to be performant and take advantage of all the data that are available to that point. Conventional intuition suggests that when solving a sequence of related optimization problems of this form, it should be possible to initialize using the solution of the previous iterate -- to start the optimization rather than initialize from scratch -- and see reductions in wall-clock time. However, in practice this warm-starting seems to yield poorer generalization performance than models that have fresh random initializations, even though the final training losses are similar. While it appears that some hyperparameter settings allow a practitioner to close this generalization gap, they seem to only do so in regimes that damage the wall-clock gains of the warm start. Nevertheless, it is highly desirable to be able to warm-start neural network training, as it would dramatically reduce the resource usage associated with the construction of performant deep learning systems. In this work, we take a closer look at this empirical phenomenon and try to understand when and how it occurs. We also provide a surprisingly simple trick that overcomes this pathology in several important situations, and present experiments that elucidate some of its properties.
Towards the fiber-optic Kennedy quantum receiver

We consider practical aspects of using standard fiber-optic elements and superconducting nanowire single-photon detectors for the development of a practical quantum receiver based on the Kennedy scheme. Our receiver allows to discriminate two phase-modulated coherent states of light at a wavelength of 1.5 microns in continuous mode with bit rate 200 Kbit/s and error rate about two times below the standard quantum limit.
Hybrid approach of the fuzzy C-Means and the K-Nearest neighbors methods during the retrieve phase of dynamic case based reasoning for personalized Follow-up of learners in real time

&lt;span&gt;The goal of adaptive learning systems is to help the learner achieve their goals and guide their learning. These systems make it possible to adapt the presentation of learning resources according to learners' needs, characteristics and learning styles, by offering them personalized courses. We propose an approach to an adaptive learning system that takes into account the initial learning profile based on Felder Silverman's learning style model in order to propose an initial learning path and the dynamic change of his behavior during the learning process using the Incremental Dynamic Case Based Reasoning approach to monitor and control its behavior in real time, based on the successful experiences of other learners, to personalize the learning. These learner experiences are grouped into homogeneous classes at the behavioral level, using the Fuzzy C-Means unsupervised machine learning method to facilitate the search for learners with similar behaviors using the supervised machine learning method K- Nearest Neighbors.&lt;/span&gt;
A new methodology for early warning of critical academic performance, based on discrete predictive models

It is widely demonstrated that academic performance depends on many different factors, and those have a different specific weight depending on the evaluated subject or the academic level.
EarthPy: A Python package that makes it easier to explore and plot raster and vector data using open source Python tools.

EarthPy makes commonly performed spatial data exploration tasks easier for scientist by building upon functions in the widely used packages: Rasterio and GeoPandas.EarthPy is designed for users who are new to Python and spatial data with a focus on scientific data.When a user is working with spatial data for research, there are a suite of data exploration activities that are often performed including:
IRIC: An R library for binary imbalanced classification

Imbalanced classification is a challenging issue in data mining and machine learning, for which a large number of solutions have been proposed. In this paper, we introduce an R library called IRIC, which integrates a wide set of solutions for imbalanced binary classification. IRIC not only provides a new implementation of some state-of-art techniques for imbalanced classification, but also improves the efficiency of model building using parallel techniques. The library and its source code are made freely available.
Cloud Instance Selection Using Parallel K-Means and AHP

Managing cloud spend and qualities when selecting cloud instances is cited as one of the timely research challenges in cloud computing. Cloud service consumers are often confronted by too many options and selection is challenging. This is because instance provision can be difficult to comprehend for an average technical user and tactics of cloud provider are far from being transparent biasing the selection. This paper proposes a novel cloud instance selection framework for finding the optimal IaaS purchase strategy for a VARD application in Amazon EC2. Analytical Hierarchy Process (AHP) and parallel K-Means Clustering algorithm are used and combined in Cloud Instance Selection environments. It allows cloud users to get the recommendation about cloud instance types and job submission periods based on requirements such as CPU, RAM, and resource utilisation. The system leverages AHP to select cloud instance type. Besides, AHP results are used by the parallel K-Means clustering model to find the best execution time for a given day according to the user's requirements. Finally, we provide an example to demonstrate the applicability of the approach. Experiments indicate that our approach achieves better results than ad-hoc and cost-driven approaches.
WAPIml: Towards a Modeling Infrastructure for Web APIs

Web APIs are becoming key assets for any business. Most of these Web APIs are "REST-like", meaning that they adhere partially to the Representational State Transfer (REST) architectural style. The OpenAPI Initiative (OAI) was launched with the objective of creating a vendor neutral, portable, and open specification for describing REST APIs. The initiative has succeeded in attracting major companies and the OpenAPI specification has become de facto format for describing REST APIs. However, there is currently a lack of tools to provide modeling facilities for developers who want to manage and visualize their OpenAPI definitions as models and integrate them into model-based processes. In this paper, we propose WAPIml an OpenAPI round-trip tool that leverages model-driven techniques to create, visualize, manage, and generate OpenAPI definitions. WAPIml embeds an OpenAPI metamodel but also an OpenAPI UML profile to enable working with Web APIs in any UML-compatible modeling tool.
Using an Enterprise Architecture Model for GDPR Compliance Principles

Nowadays, all enterprises must take into account the legal frameworks at all levels of their organization. Over the past two years, the focus has been on the GDPR. This regulation on data and their processing activities impacts on the vision of the enterprise information system. In order to identify these impacts, it is necessary to define an approach to conciliate regulatory and business points of view. Our proposal is to use an enterprise architecture modeling approach to integrate regulatory concerns. This article describes a high-level Archimate model for implementing a GDPR compliance approach.
Cloud Crypto Land

Abstract The supposed disruptive and transformational potential of blockchain technology has received widespread attention in the media, from legislators, and from academics across disciplines. While much of this attention has revolved around cryptocurrencies such as Bitcoin, many see the true promise of blockchain technology in its potential use for transactions in traditional assets, as well as for facilitating self‐executing ‘smart contracts’, which replace vague and imprecise natural language with unambiguous computer code. This article presents a simple legal argument against the feasibility of a meaningful blockchain‐based economic system. Blockchain‐based systems are shown to be unsuitable for transactions in traditional assets, unless design choices are made which render the use of the technology pointless. The same argument is shown to apply to smart contracts. Legal and practical obstacles therefore mean that, outside its original realm of cryptocurrencies, blockchain technology is highly unlikely to transform economic interactions in the real world.
Discriminative Fast Hierarchical Learning for Multiclass Image Classification

In this article, a discriminative fast hierarchical learning algorithm is developed for supporting multiclass image classification, where a visual tree is seamlessly integrated with multitask learning to achieve fast training of the tree classifier hierarchically (i.e., a set of structural node classifiers over the visual tree). By partitioning a large number of categories hierarchically in a coarse-to-fine fashion, a visual tree is first constructed and further used to handle data imbalance and identify the interrelated learning tasks automatically (e.g., the tasks for learning the node classifiers for the sibling child nodes under the same parent node are strongly interrelated), and a multitask SVM classifier is trained for each nonleaf node to achieve more effective separation of its sibling child nodes at the next level of the visual tree. Both the internode visual similarities and the interlevel visual correlations are utilized to train more discriminative multitask SVM classifiers and control the interlevel error propagation effectively, and a stochastic gradient descent (SGD) algorithm is developed for learning such multitask SVM classifiers with higher efficiency. Our experimental results have demonstrated that our fast hierarchical learning algorithm can achieve very competitive results on both the classification accuracy rates and the computational efficiency.
Meta-Q-Learning

This paper introduces Meta-Q-Learning (MQL), a new off-policy algorithm for meta-Reinforcement Learning (meta-RL). MQL builds upon three simple ideas. First, we show that Q-learning is competitive with state of the art meta-RL algorithms if given access to a context variable that is a representation of the past trajectory. Second, using a multi-task objective to maximize the average reward across the training tasks is an effective method to meta-train RL policies. Third, past data from the meta-training replay buffer can be recycled to adapt the policy on a new task using off-policy updates. MQL draws upon ideas in propensity estimation to do so and thereby amplifies the amount of available data for adaptation. Experiments on standard continuous-control benchmarks suggest that MQL compares favorably with state of the art meta-RL algorithms.
Zeroth-Order Stochastic Compositional Algorithms for Risk-Aware Learning

We present ${\it Free-MESSAGE}^{,p}$, the first zeroth-order algorithm for (weakly) convex mean-semideviation-based risk-aware learning, which is also the first-ever three-level zeroth-order compositional stochastic optimization algorithm. Using a nontrivial extension of Nesterov's classical results on Gaussian smoothing, we develop the ${\it Free-MESSAGE}^{,p}$ algorithm from first principles and show that it essentially solves a smoothed surrogate to the original problem, the former being a uniform approximation of the latter, in a useful, convenient sense. We then present a complete analysis of the ${\it Free-MESSAGE}^{,p}$ algorithm, which establishes convergence in a user-tunable neighborhood of the optimal solutions of the original problem for convex costs, as well as explicit convergence rates for convex, weakly convex, and strongly convex costs, in a unified way. Orderwise, and for fixed problem parameters, our results demonstrate no sacrifice in convergence speed as compared to existing first-order methods, while striking a certain balance among the condition of the problem, its dimensionality, and the accuracy of the obtained results, naturally extending previous results in zeroth-order risk-neutral learning.
Real-Time Detection on Cache Side Channel Attacks using Performance Counter Monitor

Cache side-channel attack extracts secret information by monitoring cache behavior of a victim. Normally, this attack targets a L3 cache which is shared between a spy and a victim. Hence, spy can learn about secret information without perception of the victim. To resist against this attack, many detection techniques have been proposed in the literature. However, those approaches have limitation since they do not operate in real time. In this paper, we propose a realtime detection technique against cache side-channel attacks. The proposed technique performs detection of cache side-channel attacks immediately after observing variation of CPU counters. For this, Intel PCM (Performance Counter Monitor) and machine learning algorithms are used to measure the value of CPU counters. Throughout the experiment, we found several PCM counters that show changes during the attack. Based on the observation, we implemented a detecting program by using these counters. Experimental results show that the proposed detection technique has good performance for real time detection in various environments.
Playing the Sprint Retrospective: A Replication Study

The Sprint Retrospective is a vehicle for continuous process improvement. Even though it is a well established agile practice, running effective retrospective meetings is challenging. There have been a lot of identified problems that commonly occur during these meetings. To address them, Przybyłek & Kotecka [20] successfully revitalized retrospective meetings by adopting collaborative games, which represent a powerful tool in improving interactions among team members. In this paper, we report on a replication of their study in Bluebay Poland and IHS Markit Gdańsk. The received feedback confirms the original findings and indicates that game-based retrospectives improve team members’ creativity, involvement, and communication as well as produce better results than the standard retrospectives. This paper is an extended version of our previous work [25].
Two Factor Authentication using Twofish Encryption and Visual Cryptography Algorithms for Secure Data Communication

Dependence of the individuals on the Internet for performing the several actions require secure data communication. Thus, the reliable data communication improves the confidentiality. As, enhanced security leads to reliable and faster communication. To improve the reliability and confidentiality, there is dire need of fully secured authentication method. There are several methods of password protections were introduced to protect the confidentiality and reliability. Most of the existing methods are based on alphanumeric approaches, but few methods provide the dual authentication process. In this paper, we introduce improved graphical password authentication using Twofish Encryption and Visual Cryptography (TEVC) method. Our proposed TEVC is unpredictably organized as predicting the correct graphical password and arranging its particles in the proper order is harder as compared to traditional alphanumeric password system. TEVC is tested by using JAVA platform. Based on the testing results, we confirm that proposed TEVC provides secure authentication. TEVC encryption algorithm detected as more prudent and possessing lower time complexity as compared to other known existing algorithms message code confirmation and fingerprint scan with password.