Reviewerly/exHarmony huggingface dataset:
Incorporating CC2020 and SWECOM Competencies into Software Engineering Curricula: A Tutorial

Professional organizations are developing a new overview curricular document titled “Computing Curricula 2020” (CC2020). This project captures the status of computing education and practice and it suggests directions for the future of computing education as it might evolve in the 2020s and beyond. Regarding software engineering, a prior workshop held at CSEE&T 2017 became a catalyst for activities that began in the early part of 2018 by producing a set of draft software engineering competencies based on the SE2014 report. These draft competencies became a part of the CC2020 project, and they should stimulate further refinement that can form a basis for a new software engineering curricular report as a successor to SE2014. After the authors present an overview of the CC2020 project and industry expectations of software engineering graduates, all participants will engage in discussions on ways to refine existing software engineering competencies using CC2020 and the software engineering competency model (SWECOM). They will also explore ways to prepare the software engineering community for new educational curricular guidelines for the future. The authors anticipate full audience involvement and participation in formulating this vision.
Deriving Theorems in Implicational Linear Logic, Declaratively.

The problem we want to solve is how to generate all theorems of a given size in the implicational fragment of propositional intuitionistic linear logic. We start by filtering for linearity the proof terms associated by our Prolog-based theorem prover for Implicational Intuitionistic Logic. This works, but using for each formula a PSPACE-complete algorithm limits it to very small formulas. We take a few walks back and forth over the bridge between proof terms and theorems, provided by the Curry-Howard isomorphism, and derive step-by-step an efficient algorithm requiring a low polynomial effort per generated theorem. The resulting Prolog program runs in O(N) space for terms of size N and generates in a few hours 7,566,084,686 theorems in the implicational fragment of Linear Intuitionistic Logic together with their proof terms in normal form. As applications, we generate datasets for correctness and scalability testing of linear logic theorem provers and training data for neural networks working on theorem proving challenges. The results in the paper, organized as a literate Prolog program, are fully replicable. Keywords: combinatorial generation of provable formulas of a given size, intuitionistic and linear logic theorem provers, theorems of the implicational fragment of propositional linear intuitionistic logic, Curry-Howard isomorphism, efficient generation of linear lambda terms in normal form, Prolog programs for lambda term generation and theorem proving.
Model of multi-agent interaction between financial service providers and consumers

Digital retail payments have evolved during the last years mostly due to technology innovations. New actors enter the payments market, increase competition and threaten banks' dominance. Consumers' behavior is critical within this context, since they are the ones that 1 finally adopt or reject a payment innovation. In this paper we propose a model of consumer behavior towards payment system selection based on multi-agent simulation. It is based on a multi-agent platform that models' consumers as intelligent agents and formats their behavior according to the rules extracted from past payments data, experts' knowledge and marketing research. Contributions from the social science and network economics also refine the agent's behavior in order to simulate accurately the domain. This paper presents the overall model and an initial analysis of payments domain, defining major actors in the domain and their interpretation to agents
Development of an Electronic Multimedia Interactive Textbook for Physics Study at Technical Universities

Kurylenko, N. Development of an Electronic Multimedia Interactive Textbook for Physics Study at Technical Universities / N. Kurylenko, A. Bovtruk, І. Slipukhina, S. Mieniailov, P. Chernega, N.Kurylenko // ICT in Education, Research, and Industrial Applications. Proc. 16 th Int. Conf. ICTERI 2020. Volume I: Main Conference. Kharkiv, Ukraine, October 06-10, 2020. Р. 159-172 (Scopus), CEURWS.org, online, http://ceur-ws.org/Vol-2740/
Polygloss - A conversational agent for language practice

This paper explores the impact on language proficiency of comprehensible output applied in computer assisted language learning (CALL).Targeting speakers of intermediate level, we adapted a visually-grounded dialogue task, optimizing for language acquisition.The task was implemented as a mobile application where learners are organized in pairs and write short texts to play an imageguessing game, producing samples in a wide variety of languages.Following a framework for CALL evaluation, we conducted an analysis of the game and players' gains through time, including the measure of pre-trained XLM-r cross-lingual transformers' acceptability score of the samples.The results confirm the intended fit for intermediate speakers as well as reveal possible benefits for other levels.This research provides a successful case study of a multilingual CALL design where users have the autonomy to generate output creatively.
Experiential Learning for Software Engineering using Agile Modeling in Umple

In this tutorial we will discuss a method of teaching a variety of software engineering topics in an integrated and experiential manner. Topics covered include agile processes, software modeling, open source development tools and processes, code generation and testing. Our approach involves using a textual modeling language called Umple that enables agile, model-driven, test-driven development. Students are introduced to this tool in an introduction to software engineering course, where they learn the basics of the above topics. Then in their capstone course they are able to actually contribute to the tool itself.
Articulatory and acoustic phonetics of voice actors

Looking at voice actors' novel productions of typical speech sounds can tell us more about over which muscles we have volitional control. This can help us to test models of speech production, inform speech therapy, and create better pedagogy for teaching vocal performance.
Typo Correction in Domain-Specific Texts Using FastText

Analyzing customer reviews are quite important for customer satisfaction. Customer reviews might contain spelling mistakes, which causes data pollution and decreases the efficiency of the analyzes. In this study, a domain-specific solution is proposed by using the data related to tourism. Even if there are several applications to correct typos in Turkish, domain-specific solutions are limited. Since a correction should be specific for the meaning of a typo, this study is required. For the study, a FastText model-oriented typo correction algorithm has been developed by using customer reviews in the tourism industry. The results are compared with a commonly used correction application and it is observed that the algorithm developed is more successful for correcting typos in tourism specific phrases.
Problematising Hateful Ethno-Political Rhetoric in Facebook and Twitter during 2017 General Elections in Kenya

The purpose of this study is to examine online hate discourse; with a focus on the construction of online ethno-political rhetoric as a form of hate speech during Kenya's 2017 general election. The study employed a qualitative case study design which entailed an empirical investigation of a particular phenomenon using multiple evidence. Purposive sampling allowed the researcher to observe, collect and analyse only the specific materials that had the characteristics relevant to the objective of this study. Working within Computer-Mediated Discourse Analysis (CMDA) framework, we analyse a purposively selected sample of sixteen posts from FB (ten) and Twitter (six) derived from the initial sample of 360 posts collected through online observation of Facebook groups and hashtags trending in Kenya between July and November 2017. ‘The findings point at the shifting hate speech battle fields where ethno-political extremism in form of ‘Us against Them’ discourse finds easy expression online through dehumanizing epithets and metaphors that de-personalize and de-characterize the target, bringing to salience their perceived negative attributes in order to justify prejudice against them as a tool of political mobilization. These insights are relevant in understanding hate speech in multi-ethnic cultural contexts in society generally and specifically in Kenya. The study recommends that the government of Kenya and other key stakeholders should develop a media literacy policy on the moral responsibility in embracing netiquette and responsible netizenship in online interactions.
Don't Panic! Better, Fewer, Syntax Errors for LR Parsers (Artifact)

This is the artefact accompanying the paper Don't Panic! Better, Fewer, Syntax Errors for LR Parsers by Diekmann and Tratt. It focusses on the experiment from that paper, which compares a number of different error recovery algorithms on a large corpus of data, including all the software necessary to reproduce the experiment from the paper.
Identifying icequakes at ice-covered volcanoes in Southern Chile

&amp;lt;p&amp;gt;Volcanoes and glaciers are both productive sources of seismic activity which may be easily confused for each other, leading to potential missed warnings or false alarms. This presents a challenge for organizations monitoring active volcanoes with glaciers on or near the edifice. Cryogenic earthquakes (i.e. icequakes) have been studied at only a few volcanoes around the world and there is a ready need to develop robust methods for efficiently differentiating them from volcanic events. Here we present results from an ongoing study of icequakes at active ice-covered volcanoes in the Southern Chilean Volcanic Zone. The primary focus of the project so far has been on seismo-acoustic data collected at Llaima volcano, one of the largest and most active volcanoes in the region. The data, recorded in 2015 and 2019, was analysed using a combination of automatic multi-station event detection and waveform cross-correlation to find candidate repeating icequakes. We identified 11 persistent families of repeating events in 2015, and over 50 families in 2019; the largest family containing over 1000 events from January to April 2019. The persistent, repetitive nature of these events combined with their waveform characteristics and source locations suggest they originated from multiple sub-glacial sources on the upper flanks of the volcano. Low levels of volcanic activity at Llaima volcano since 2009 have allowed this clear discrimination of icequake events. We are also targeting Villarrica volcano in early 2020 with a network of seismo-acoustic sensors and to record icequake activity in concurrence with the ongoing eruptive activity at the summit. Altogether, the results from this project so far suggest icequakes may be more common than previously thought and has strong implications for how seismic data at ice-covered volcanoes may be interpreted.&amp;lt;/p&amp;gt;
The Impact of Transformational Leadership on Crisis Management in the Cellular Communications Sector in Jordan

The current study aimed to identify the impact of transformational leadership on crisis management in the Cellular Communications sector in Jordan. This study relies on the descriptive and analytical approach for this study.&#x0D; The study population includes administrators working in Zain, Orange, and Umniah companies. A Convenience non-probability sample of 100 managers was taken to distribute the study questionnaire to them.&#x0D; Moreover, the study reached the following results: There is a statistically significant impact at (&amp;alpha; &amp;le; 0.05) for transformational leadership with its dimensions (ideal effect, inspirational stimulation, intellectual stimulation, individual considerations) on crisis management in Jordan cellular communications sector.&#x0D; In light of the previous results, the researcher recommends the following: The necessity for the administration to use the information network security system to retrieve information before the crisis occurs. Senior management&amp;#39;s necessity to benefit from its previous experiences to generate knowledge benefits the organization&amp;#39;s employees&amp;mdash;the need to create an organizational culture that promotes the useful application of transformational leadership.
Reflections on Teaching Architecture Design

Architecture design is notoriously difficult to teach and to learn. Most competent architects in industry have deep knowledge won from long years of experience. But if we want architecture design to be methodical and repeatable, we need better ways of teaching it. Simply waiting for an aspiring young architect to accumulate 10 or 20 years of experience is not acceptable if we believe that software engineering is a true engineering discipline that can be taught and learned in a reasonable time-frame. In this talk I will describe my experiences based on over 20 years of teaching software architecture design (and analysis), to both undergraduates and industry professionals. I will reflect on three aspects of teaching design, each of which was a major “lesson learned” for me: 1) the need for a methodical approach to design, 2) role-playing and game-play as a means of engaging learners to learn a method, and 3) tool-support for teaching.
Simulation of optimal solutions for assignment problems in the context of incomplete information

In this paper, we construct a game-theoretic model related to the problem of optimal assignments. A number of examples are considered in which a compromise point, Nash equilibrium and Pareto optimum are found. For the case of six players, three models were constructed for which solutions of the assignment problem are found based on different optimality principles.
Augmented Reality dalam Pembelajaran

Augmented reality is one of the developing technologies and has a good potential to apply in the teaching and learning process. Augmented reality facilitates and improves the quality of teaching and learning process because of the combination of virtual objects with real environments by taking advantage of the use of technology. Augmented reality visualizes abstract concepts for understanding and the structure of an object model. A main characteristic for a medium classified as an augmented reality is combining real and virtual objects, integrated in real and virtual objects, and a real-time interaction. The use of augmented reality in teaching and learning has already been applied to many subjects, such as the concepts of space and geometry in mathematics, the human digestive system of biology, the introduction of traditional houses in social science, and the introduction of wudhu procedure in early childhood education or kindergarten (PAUD/TK) kids.
Towards GPU Accelerated HyperSpectral Depth Estimation in Medical Applications

HyperSpectral (HS) images are commonly used for classification tasks in different domains, such as medicine. In this field, a recent use is the differentiation between healthy tissues and different types of cancerous tissues. To this end, different machine learning techniques have been proposed to generate classification maps that indicate the type of tissue corresponding to each pixel in the image. These 2D representations can be used stand-alone, but they can not be properly registered with other valuable data sources like Magnetic Resonance Imaging (MRI), which can improve the accuracy of the system. For this reason, this paper builds the foundations of a multi-modal classification system that will incorporate 3D information into HS images. Specifically, we address the acceleration of one of the hotspots in depth estimation tools/algorithms. MPEG-I Depth Estimation Reference Software (DERS) provides high-quality depth maps relying on a global energy optimizer algorithm: Graph Cuts. However, this algorithm needs huge processing times, preventing its use during surgical operations. This work introduces GoRG (Graph cuts Reference depth estimation in GPU), a GPU accelerated DERS able to produce depth maps from RGB and HS images. In this paper, due to the lack of HS multi-view datasets at the moment, results are reported on RGB images to validate the acceleration strategy. GoRG shows a ×25 average speed-up compared to baseline DERS 8.0, reducing total computation time from around one hour for 8 frames to only a few minutes. A consequence of our parallelization is an average decrease of 1.6 dB in Weighted-to-Spherically-Uniform Peak-Signal-to-Noise-Ratio (WS-PSNR), with some remarkable disparities approaching 4 dB. However, using Structural Similarity Index (SSIM) as metric results come closer to baseline DERS. Effectively, an average decrease of only 1.20% is achieved showing that the obtained speed-up gains compensate the subjective quality losses.
Secure Chaotic Image Encryption Based on Multi-Point Row-Column-Crossover Operation

In this present era, where everything is getting digitalized, information or data in any form, important to an organization or individual, are at a greater risk of being attacked under acts, commonly known as cyber-attack. Hence, a proper and more efficient cryptosystem is the prime need of the hour to secure the data (especially the image data). This chapter proposes an efficient multi-point crossover operation-based chaotic image encryption system to secure images. The multi-point crossover operation is performed on both the rows and columns of bit-planes in the images. The improved one-dimensional chaotic maps are then used to perform pixel-permutation and diffusion operations. The main advantage of this technique is the use of multi-point crossover operation in bit-levels. The multi-point crossover operation not only increases the security of cipher images but also increases the key space of the algorithm. The outcomes and analyses of various parameters show the best performance of the algorithm in image encryption and different common attacks.
Pengenalan Gerak Manusia Menggunakan Algoritma Relevance Vector Machine pada MSRC-12 Dataset

Relevance vector machine is a popular machine learning technique that is motivated by statistical learning theory. RVM can be used for gesture recognition which is one of the communication tools used by humans. This study proposes an experiment using the Relevance Vector Machine (RVM) algorithm on gesture data from Microsoft Research Cambridge-12 (MSRC-12) as a proposed solution to overcome unbalanced problems in data processing. The results of the study are the accuracy for 1-person motion model reaches 100% and the lowest accuracy with 5 people the motion model reaches 96%. Graphically, the more people or models, the lower the algorithm's accuracy.
“J’veux du Soleil” Towards a Decade of Solar Irradiation Data (La Réunion Island, SW Indian Ocean)

This paper aims at presenting years of solar irradiation data together with meteorological data acquisition localized in the French region of La Réunion Island (SW Indian Ocean). The publicly available data take the form of multivariate time series data with one-minute sampling rate over eight years – with still ongoing acquisition. We also present typical analytics tasks that are related to solar energy application domain as well as general time series analytics tasks that are suitable for these data. Thus, we aim at drawing the attention of the time series data mining community to these valuable data.
Penggunaan Novel Tetralogi Laskar Pelangi dalam Pengembangan Bahan Ajar Menulis Teks Deskripsi untuk Siswa SMP Kelas VII

&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; A novel is a literature work of fiction which tells about the life of a person and the people around him which are presented in a series of stories that are more complex. A novel can be used as a source of learning for students. This study uses a novel as the source of learning to develop teaching material. This study aims at producing teaching material which describes the setting, characters, and events being told in the novel. This study is a research and development study which uses a 4-D model. The model covers four main stages namely define, design, develop, and disseminate. The teaching material that has been compiled contains activities, steps, exercises, revisions, and peer-assessments in writing descriptive text about setting, characters, and events by using quotations written in the tetralogy of Laskar Pelangi. The activities, steps, and exercises in the teaching material will help the students in writing descriptive text well and detailed.&lt;/p&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt; Novel merupakan karya fiksi dengan rangkaian cerita kehidupan seseorang dengan orang lain di sekelilingnya yang ditampilkan lebih kompleks. Novel dapat digunakan sebagai sumber belajar bagi peserta didik. Penelitian ini menggunakan novel untuk mengembangkan bahan ajar sebagai sumber belajar peserta didik. Penelitian ini bertujuan untuk menghasilkan bahan ajar mendeskripsikan latar dalam novel, menghasilkan bahan ajar mendeskripsikan tokoh dalam novel, dan menghasilkan bahan ajar mendeskripsikan peristiwa dalam novel. Penelitian ini merupakan penelitian dan pengembangan dengan menggunakan model pengembangan 4-D, yaitu &lt;em&gt;define &lt;/em&gt;(penetapan), &lt;em&gt;design&lt;/em&gt; (perancangan), &lt;em&gt;develop&lt;/em&gt; (pengembangan), dan &lt;em&gt;disseminate &lt;/em&gt;(penyebaran). Bahan ajar yang telah disusun berisi kegiatan, langkah-langkah, latihan, revisi, dan penyuntingan teman sejawat dalam menulis teks deskripsi latar, tokoh, dan peristiwa dengan menggunakan kutipan-kutipan dari novel tetralogi Laskar Pelangi. Kegiatan, langkah-langkah, dan latihan dalam bahan ajar tersebut akan membantu peserta didik dalam menulis teks deskripsi yang baik, benar, dan terperinci.
CAREA: Cotraining Attribute and Relation Embeddings for Cross-Lingual Entity Alignment in Knowledge Graphs

Knowledge graphs (KGs) are one of the most widely used techniques of knowledge organizations and have been extensively used in many application fields related to artificial intelligence, for example, web search and recommendations. Entity alignment provides a useful tool for how to integrate multilingual KGs automatically. However, most of the existing studies evaluated ignore the abundant information of entity attributes except for entity relationships. This paper sets out to investigate cross-lingual entity alignment and proposes an iterative cotraining approach (CAREA) to train a pair of independent models. The two models can extract the attribute and the relation features of multilingual KGs, respectively. In each iteration, the two models alternate to predict a new set of potentially aligned entity pairs. Besides, this method further filters through the dynamic threshold value to enhance the two models’ supervision. Experimental results on three real-world datasets demonstrate the effectiveness and superiority of the proposed method. The CAREA model improves the performance with at least an absolute increase of 3.9 <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"><mo>%</mo></math> across all experiment datasets. The code is available at https://github.com/ChenBaiyang/CAREA.