Reviewerly/exHarmony huggingface dataset:
CyberChallenge.IT@Unige

We present our experience as educators of small groups of students interested in cybersecurity and ethical hacking. Since 2018 we have been involved in a national cybersecurity training program whose primary goal is bringing talented young students to this field and lessen the gap between the available workforce and what the market demands. The training model exploits gamification principles, and our students apply their knowledge and skills in online competitions, playing in virtual arenas. These provide lawful environments to experiment with cybersecurity vulnerabilities, attacks, and defenses freely and legally. In this paper we first describe the national program we are involved in, and then we detail the activities taken at our university, with a special emphasis on this year edition that, due to the COVID-19 restrictions, is currently running entirely online.
A Stable Personalised Partner Selection for Collaborative Privacy Education

Privacy education is becoming increasingly important these days, especially for young people. While several e-learning platforms for privacy awareness training have been implemented, they are typically based on traditional learning techniques. More specifically, they do not allow students to cooperate and share knowledge in order to achieve mutual benefits and improve learning outcomes. In this paper, we propose a collaborative e-learning platform for privacy education, which can provide a stable personalized partner selection mechanism using game theory. The proposed mechanism guarantees a stable student-student matching according to students' preferences (behavior and/or knowledge). Experimental results show the effectiveness of the proposed model in terms of achieving students' satisfaction compared to other existing partner selection models. The results also suggest that the proposed approach allows us to achieve better learning outcomes in privacy education.
Computational paths - a weak groupoid

Abstract On the basis of a labelled deduction system (LND$_{ED-}$TRS), we demonstrate how to formalize the concept of computational paths (sequences of rewrites) as equalities between two terms of the same type. This has allowed us to carry out a formal counterpart to equality between paths which is dealt with in homotopy theory, but this time with an approach using the device of term-rewriting paths. Using such formal calculus dealing with paths, we construct the fundamental groupoid of a path-connected $ X $ type and we define the concept of isomorphism between types. Next, we show that the computational paths determine a weak category, which will be called $ \mathcal {C}_{paths} $. Finally, we show that the weak category $ \mathcal {C}_{paths} $ determines a weak groupoid.
Emotion Classification with Reduced Feature Set SGDClassifier, Random Forest and Performance Tuning

Text Classification is vital and challenging due to varied kinds of data generated these days; emotions classification represented in form of text is more challenging due to diverse kind of emotional content and such content is growing on web these days. This research work is classifying emotions written in Hindi in form of poem with 4 categories namely Karuna, Shanta, Shringar and Veera. POS tagging is used on all the poem and then features are extracted by observing certain poetic features, two types of features are extracted and the results in terms of accuracy is measured to test the model. 180 Poetries were tagged and features were extracted with 8 different keywords, and 7 different keywords. The model is build with Random Forest, SGDClassifier and was trained with 134 poetries and tested with 46 Poetries for both types of features. The results with 7 keyword feature is comparatively better than 8 keyword feature by 7.27% for Random Forest and 10% better for SGDClassifier. Various combinations of hyper parameters are used to get the best results for statistical measure precision and recall for performance tuning of the model. The model is also tested with k – fold cross validation with average result 62.53% for 4 folds and 60.45% for 8 folds with Random Forest and 54.42% for 4 folds and 48.28% for 8 folds with SGDClassifier, the experimentation result of Random Forest is better than SGDClassifier on the given dataset.
Blockchain-based Data Protection Architecture for Internet of Things

With the development of Internet of Things (IoT), the IoT data security issues have raised great attention of academics and industry. However, due to the IoT essential characteristics of decentralization and distribution, traditional centralized architecture suffers from security and scalability challenges. The blockchain technology has been widely applied in industrial domains such as finance and logistics because of its key features of decentralization, data traceability, immutability and auditability which will greatly benefit the IoT system. In this paper, the foundamental of blockchain technology is summerized and the blockchain-based data protection architecture for IoT is proposed. The data transmission, storage and sharing schemes are detailed discussed and evaluated. The result indicates that the implement of blockchain technology will prevent typical attacks and bring benefit to data security of IoT.
Self-Adaptation with Imperfect Monitoring in Solar Energy Harvesting Systems

Ubiquitous and perpetual nature of cyber-physical systems (CPSs) have made them mostly battery-operated in many applications. The batteries need recharge via environmental energy sources. Solar energy harvesting is a conventional source for CPSs, whereas it is not perfectly predictable due to environmental changes. Thus, the system needs to adaptively control its consumption with respect to the energy harvesting. In this paper, we propose a model-driven approach for analyzing self-adaptive solar energy harvesting systems; it uses a feedback control loop to monitor and analyze the behavior of the system and the environment, and decides which adaptation action must be triggered against the changes. We elaborate a data-driven method to come up with the prediction of the incoming changes, especially those from the environment. The method takes the energy harvesting data for prediction purposes, and models the environment as a Markov chain. We empower the proposed system against the runtime monitoring faults as well. In this regard, the system is able to verify an incomplete model, i.e. when some data is missed. To this aim, we propose a pattern-matching system that simulates the current behavior of the system using random walk, and matches it with the history to estimate the omitted data. The results show an accuracy of at least 96% when decisions are made by imperfect monitoring.
Resolution Irrelevant Encoding and Difficulty Balanced Loss Based Network Independent Supervision for Multi-Person Pose Estimation

Sustainable efforts are made to improve the accuracy performance in multi-person pose estimation, but the current accuracy is still not enough for real-world applications. Besides, most improvement approaches are designed for special basement networks and ignore the speed performance, which results in limited applicability and low cost-performance. This paper proposes two network independent supervision: Resolution Irrelevant Encoding and Difficulty Balanced Loss. The proposed methods reorganize task representatives, the loss calculation method, and the loss punishment ratio in one-stage pose estimation frameworks to improve the joints' location accuracy with general applicability and high computational efficiency. Resolution Irrelevant Encoding fuses heatmaps and proposed inner block offsets to fix pixel-level joints positions without resolution limitations. To improve network training efficiency, Difficulty Balanced Loss adjusts loss weight in spatial and sequential aspects. On the MS COCO keypoints detection benchmark, the mAP of OpenPose trained with our proposals outperforms the OpenPose baseline over 4.9%.
The Banking Industry Underestimates Costs of Cloud Migrations

Cloud computing is a growing part of the IT industry and is seen collectively as a solution to manage data and systems effectively. In numerous areas, cloud computing is being used as the standard for deploying application areas include; banking, telecommunications, and logistics. However, migrating IT infrastructure and applications to the cloud can induce hidden and overlooked costs. This study examines the cloud migration process of ten international corporate banks, to explore and understand costs occurring during a cloud migration. We have identified that the banking industry underestimates cloud migration costs and that budgets are exceeded. The top five cost categories that are underestimated: (1) managing dependencies of applications, (2) legislation, (3) support required from other non IT departments, (4) re-architecting of applications, and (5) hiring of external contractors. Main reasons for overspending are: (1) lack of proper and rigorous business cases, (2) knowledge deficit related to cloud migrations and the corresponding IT-architecture, and (3) no clear cloud migration strategy. Cloud computing is a transformative technology which requires a rigorous approach to remain within budget and deliver the desired results.
Case-Study-Based Review of Approaches for Transforming UML Class Diagrams to OWL and Vice Versa

Building ontologies in The Web Ontology Language (OWL) as a knowledge representation about a particular domain gained a lot of interest over the recent years. Thanks to its large community and many options concerning tooling and methods for representation and transformations, OWL is being used not only in Linked Open Data and Artificial Intelligence but also in conceptual modelling. OWL allows capturing concepts and their properties, including relationships which can also be done using traditional conceptual models, for example, in Unified Modelling Language (UML). Both UML and OWL have their own specifics when compared to each other, and one may be more suitable than the other in concrete cases. There are several methods for transformation between knowledge representation in OWL and UML. In this paper, we review key methods for transforming UML to OWL and vice versa. To compare the methods, we use a non-trivial conceptual model that contains all commonly used constructs, e.g., generalization sets, composition, or relationships with constraints. The methods are evaluated in terms of information loss during transformation, the need for human intervention, and versatility.
Towards the development of future trend scenarios through dynamic analysis : A proposal of integration of Artificial Intelligence, Data Sciences and the field of Futures Studies to adapt to new environments

The paper discusses the role of integrating Artificial Intelligence (AI) and Data Science (DS) with the field of Futures Studies. That is, combining the art of conjecture with several algorithms available in the literature. The combination between qualitative and quantitative methods offers a new dynamic adaptation route to face the rapidly changing environment, to make strategic design more flexible in the long term. The proposal uses the ISSM framework [36] as a notion of a strategic Early Warning System (SEWS) [31]. The purpose is help to develop the component named competitive intelligence (CI) architecture to create knowledge about the future, improving continuous learning, and promote the capacity to adaptation. Therefore, anticipation, learning and strategic adaptation, are key attributes for building strategic flexibility that responds to several challenges.
Efficient Minimum Distance Computation for Solids of Revolution

Abstract We present a highly efficient algorithm for computing the minimum distance between two solids of revolution, each of which is defined by a planar cross‐section region and a rotation axis. The boundary profile curve for the cross‐section is first approximated by a bounding volume hierarchy (BVH) of fat arcs. By rotating the fat arcs around the axis, we generate the BVH of fat tori that bounds the surface of revolution. The minimum distance between two solids of revolution is then computed very efficiently using the distance between fat tori, which can be boiled down to the minimum distance computation for circles in the three‐dimensional space. Our circle‐based approach to the solids of revolution has distinctive features of geometric simplification. The main advantage is in the effectiveness of our approach in handling the complex cases where the minimum distance is obtained in non‐convex regions of the solids under consideration. Though we are dealing with a geometric problem for solids, the algorithm actually works in a computational style similar to that of handling planar curves. Compared with conventional BVH‐based methods, our algorithm demonstrates outperformance in computing speed, often 10–100 times faster. Moreover, the minimum distance can be computed very efficiently for the solids of revolution under deformation, where the dynamic reconstruction of fat arcs dominates the overall computation time and takes a few milliseconds.
Preference Cognitive Diagnosis for Predicting Examinee Performance

Cognitive diagnosis can discover the examinees' knowledge state for predicting their performance (i.e., scores). However, cognitive modeling only considers the examinees' response logs on exercises, so the prediction accuracy cannot be guaranteed. Actually, examinees usually read some text learning materials before responding exercises to enhance their performance. These specific learning materials will reveal examinees' learning preferences, which will also reflect their knowledge state to some extent. To this end, we propose a Preference Cognitive Diagnosis (Preference CD) method, which combines learning preferences with cognitive diagnosis to evaluate examinees' knowledge state so as to gain accurate prediction results. Specifically, first, we utilize the cognitive diagnosis model to obtain examinees' mastery for exercises. Then, we establish the keyword vector model that can extract examinees' learning preferences information from the contents of learning material read by examinees. Further, we quantify the extracted examinees' learning preferences information as their preference degree for exercise. Finally, we project the generation of exercise scores by combining examinees' mastery and preference degree for exercises. Experimental results on learning behavior data from the actual operating system show PreferenceCD can predict examinees' performance more accurately.
Adaptation oriented test data generation for Adaptive Systems

The complexity of modern and emerging software systems has steadily increased. These systems must adapt to preserve their operation in the presence of uncertain changes. One of the techniques of quality assurance is software testing. The generation of test data sets presents a great challenge for the test activity due to the multiple inputs, combinations of variables and dependence on the environmental information in which the adaptive system is inserted. This research presents an approach, called AGAS, for the generation of test data sets for adaptive systems. AGAS uses the concept of virtual environments, the changes in these environments and the implications of system adaptations and works with the interaction of the control loop of an adaptive system. An experimental study was carried out to compare AGAS with the randomized approach using three assessment metrics. The approach was applied to three examples of adaptive systems. The results obtained showed that there was a considerable gain in the generation of the test data set, occurrences of adaptations and adaptations with errors. AGAS suggest that it is a promising approach to generate test data sets for adaptive systems considering the adaptation criterion.
Mapping of the Perception of Theft Crimes from Analysis of Newspaper Articles Online

The analysis of crimes represents a great challenge to law enforcement agencies and organizations that collect information to analyze accurately and effectively the distribution of the behavior of criminal events, considering that the sources to be used in the process of generating intelligence are diverse in content and/or structure. Tools that have contributed to the analysis of fields in which massive amounts of data are available is "data mining", a powerful tool that can be used effectively in the analysis of large amounts of data and in the subsequent derivation of important analytical results. This paper presents a methodology of analysis of crime facts from online newspapers, identifying the different communes where the greatest number of criminal events occur, which gives an idea of potentially more dangerous places, through the detection and geographical mapping of critical points, or the analysis of the nature of the crime through the extraction of entities. Statistics that measure the predictive capacity of the model indicate that the methodology is robust to recognize crime events within the body of the news.
Obstacles to the Transformation of Scientific and Technological Achievements and Countermeasures Based on Computer Technology

Abstract As the environment and situation changes in the transformation of scientific and technological achievements, the decisive role of the market in the transformation of scientific and technological achievements has become increasingly critical. The insufficiency of some existing technologies in China has become the institutional obstacles to promoting the transformation of scientific and technological achievements. In this paper, the domestic transformation technologies of scientific and technological achievements are consolidated. The institutional factors that constrain the transformation of scientific and technological achievements in China under the new situation are analyzed. The countermeasures and suggestions for building the transformation system of scientific and technological achievements in China are proposed from the perspectives of playing the role of the market, improving the transformation and disposal right for scientific and technological achievements, changing the scientific and technological evaluation system, valuing the overall technological coordination, reforming the evaluation mechanism, etc.
Deep Learning-Based Automatic Pronunciation Assessment for Second Language Learners

For second language learners, computer-aided language learning (CALL) is of high importance. In recent years, the use of smart phones, tablets, and laptops has become increasingly popular; with this change, more people can use CALL to learn a second language. In CALL, automatic pronunciation assessment can be applied to provide feedback to teachers regarding the efficiency of teaching approaches. Furthermore, with automatic pronunciation assessment, students can monitor their language skills and improvements over time while using the system. In the current study, a text-independent method for pronunciation assessment based on deep neural networks (DNNs) is proposed and evaluated. In the proposed method, only acoustic features are applied, and native acoustic models and teachers’ reference speech are not required. The method was evaluated using speech from a large number of Japanese students who studied English as a second language.
Adding an Abstraction Barrier to ZF Set Theory

Much mathematical writing exists that is, explicitly or implicitly, based on set theory, often Zermelo-Fraenkel set theory (ZF) or one of its variants. In ZF, the domain of discourse contains only sets, and hence every mathematical object must be a set. Consequently, in ZF with the usual encoding of an ordered pair $$\langle a,b\rangle $$ , formulas like $$\{a\} \in \langle a,b\rangle $$ have truth values, and operations like $$\mathcal {P}{(\langle a,b\rangle )}$$ have results that are sets. Such ‘accidental theorems’ do not match how people think about the mathematics and also cause practical difficulties when using set theory in machine-assisted theorem proving. In contrast, in a number of proof assistants, mathematical objects and concepts can be built of type-theoretic stuff so that many mathematical objects can be, in essence, terms of an extended typed $$\lambda $$ -calculus. However, dilemmas and frustration arise when formalizing mathematics in type theory. Motivated by problems of formalizing mathematics with (1) purely set-theoretic and (2) type-theoretic approaches, we explore an option with much of the flexibility of set theory and some of the useful features of type theory. We present ZFP: a modification of ZF that has ordered pairs as primitive, non-set objects. ZFP has a more natural and abstract axiomatic definition of ordered pairs free of any notion of representation. This paper presents axioms for ZFP, and a proof in ZF (machine-checked in Isabelle/ZF) of the existence of a model for ZFP, which implies that ZFP is consistent if ZF is. We discuss the approach used to add this abstraction barrier to ZF.
Apples and Oranges

This poster paper presents a proposal for the usability evaluation contrasting a VUI with a GUI. The proposal was pilot tested with six users performing six tasks. The setting was controlled, and sessions were video-recorded for the extraction and analysis of indicators which were cross-validated with subjective user data. Results suggest that the framework is effective for the conditions given and viable for further development.
End-User-Oriented Tool Support for Modeling Data Analytics Requirements

Big data and analytics are increasingly used in different domains to gain insights and to improve decision-making. Developing big data analytics solutions is a complex task involving multidisciplinary teams and users - with no data science and programming background - to professional data scientists and software engineers. Different stakeholders work with a variety of data types, tasks and concepts in different languages from high- level domain concepts to low level programming languages and technical concepts. In order to advance the level of abstraction beyond low-level data analysis technical details, we demonstrate our BiDaML tool. BiDaML brings all stakeholders around one tool to specify, model and document their big data applications using a novel set of domain-specific visual languages (DSVLs).
Improving Cooperation Between Spatially Separated Operators Using Augmented Reality

The technologies for the use of augmented reality are maturing more and more and seem to offer many possible applications. There is also a lot of research and development in the industrial and business environment to increase productivity, improve communication or minimize errors. This work focuses on the use of augmented reality in the cooperation between spatially separated operators working on a common task. AR is intended to present spatial information to the operator being instructed, thus relieving the operator of tasks involving spatial orientation. In a study that is currently being planned, we want to find out whether the use of AR can support the operator in a search-and-rescue scenario at sea and thus reduce the time needed to perform tasks. AR will be compared to the way information is presented on traditional displays.