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A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Master’s of Science in Mathematical and Computer Science and Engineering of the Nelson Mandela African Institution of Science and Technology

Шмелева Евгения Дмитриевна - научный сотрудник Центра социологии высшего образования Института образования Национального исследовательского университета «Высшая школа экономики». E-mail: eshmeleva@hse.ruФрумин Исак Давидович - научныи руководитель Института образования Национального исследовательского университета«Высшая школа экономики». E-mail: ifroumin@hse.ruАдрес: 101000, Москва, ул. Мясницкая, 20.Инженерному образованию в России сегодня уделяется особое внимание: около половины всех бюджетных мест в вузах выделяется на инженерные направления подготовки. И в то же время среди студентов этого профиля наблюдается наиболее высокий уровень выбытия. Цель данного исследования - оценить масштаб выбытия студентов инженерно-технического профиля в начале и середине обучения, а также установить факторы выбытия. Исследование опирается на данные, полученные в ходе опроса более 4 тыс. студентов инженерных направлений подготовки в 34 российских вузах, составляющих репрезентативную национальную выборку, а также на административные сведения об отчислениях студентов. При анализе факторов выбытия студентов в первые три семестра обучения авторы опираются на теоретическую рамку В. Тинто, согласно которой социальная и академическая интеграция студентов является критически важным условием успешного продолжения обучения в выбранном университете. Результаты исследования подтверждают ключевую роль академической интеграции, а именно посещения занятий и активного взаимодействия с преподавателями, в предотвращении выбытия студентов, но опровергают гипотезу о значимости социальной интеграции. Риску выбытия подвергаются студенты с низкими баллами ЕГЭ по математике, а также те, чей фактический выбор направления подготовки не соответствовал желаемому. Гипотеза о том, что уровень выбытия в высокоселективных вузах выше, чем в прочих, не нашла подтверждения. Предложены рекомендации для университетов, направленные на снижение уровня выбытия среди студентов.

We have discussed in this elucidation paper about correlation of Fibonacci sequence and golden ratio with its applications in engineering and science. One of the most recurring sequences in nature is the Fibonacci sequence. As the sequence was explored, it was found out that this sequence led to the golden ratio. This study tried to apply the concept of Fibonacci and golden ratio to maximize efficiency of our live life. We consider self-similar curve like golden spiral in whose nature their beauty is much admired. The explanations show that source of Fibonacci numbers and how to exist Fibonacci numbers in the world we live. The mathematical theories of Fibonacci numbers and golden ratio gives the source of many new ideas in Mathematics, Chemistry, Civil engineering, Architecture, Automobile engineering, Philosophy, Botanic and biology, Electrical engineering, Computer science and engineering, Mechanical engineering, Communication systems, Mathematical education as well as theoretical physics and physics of high energy particles.

Significance Societal stereotypes that girls are less interested than boys in computer science and engineering are endorsed by children and adolescents in a large and socioeconomically diverse sample, across multiple racial/ethnic and gender intersections, and as early as age six (first grade). Gender-interest stereotypes may contribute to subsequent gender disparities in the pursuit of these societally important fields. Addressing interest stereotypes may help improve educational equity., Societal stereotypes depict girls as less interested than boys in computer science and engineering. We demonstrate the existence of these stereotypes among children and adolescents from first to 12th grade and their potential negative consequences for girls’ subsequent participation in these fields. Studies 1 and 2 (n = 2,277; one preregistered) reveal that children as young as age six (first grade) and adolescents across multiple racial/ethnic and gender intersections (Black, Latinx, Asian, and White girls and boys) endorse stereotypes that girls are less interested than boys in computer science and engineering. The more that individual girls endorse gender-interest stereotypes favoring boys in computer science and engineering, the lower their own interest and sense of belonging in these fields. These gender-interest stereotypes are endorsed even more strongly than gender stereotypes about computer science and engineering abilities. Studies 3 and 4 (n = 172; both preregistered) experimentally demonstrate that 8- to 9-y-old girls are significantly less interested in an activity marked with a gender stereotype (“girls are less interested in this activity than boys”) compared to an activity with no such stereotype (“girls and boys are equally interested in this activity”). Taken together, both ecologically valid real-world studies (Studies 1 and 2) and controlled preregistered laboratory experiments (Studies 3 and 4) reveal that stereotypes that girls are less interested than boys in computer science and engineering emerge early and may contribute to gender disparities.

Here we introduce a new column that explores IEEE Computer Magazine’s contribution to the computing literature and how it is laying the foundation for the future of computer science and engineering.

Nanotechnology is the science of objects

Data is the most crucial component of a successful ML system. Once a machine learning model is developed, it gets obsolete over time due to presence of new input data being generated every second. In order to keep our predictions accurate we need to find a way to keep our models up to date. Our research work involves finding a mechanism which can retrain the model with new data automatically. This research also involves exploring the possibilities of automating machine learning processes. We started this project by training and testing our model using conventional machine learning methods. The outcome was then compared with the outcome of those experiments conducted using the AutoML methods like TPOT. This helped us in finding an efficient technique to retrain our models. These techniques can be used in areas where people do not deal with the actual working of a ML model but only require the outputs of ML processes.

Color is considered to be a major characteristic feature that is used for distinguishing benign and malignant melanocytic lesions. Most of malignant melanomas are characterized by the presence of six suspicious colors inspired from the ABCD dermoscopic rule. The presence of these suspicious colors histopathologically indicates the presence of melanin in the deeper layers of the epidermis and dermis. The objective of the proposed work is to evaluate the role of color features, a set of fifteen color features have been extracted from the region of interest to determine the role of color in malignancy detection. Further, a set of ensemble classifiers with dynamic selection techniques are used for classification of the extracted features, yielding an average accuracy of 87.5% for classifying benign and malignant lesions.

Machine Translation Systems are part of Natural Language Processing (NLP) that makes communication possible among people using their own native language through computer and smart media devices. This paper describes recent progress in language dictionaries and machine translation commonly used for communications and social interaction among people or Internet users worldwide who speak different languages. Problems of accuracy and quality related to computer translation systems encountered in web & Apps-based translation are described and discussed. Possible programming solutions to the problems are also put forward to create software tools that are able to analyze and synthesize language intelligently based on semantic representation of sentences and phrases. Challenges and problems on Apps-based machine translation on smart devices towards AI, NLP, smart learning and understanding still remain until now, and need to be addressed and solved through collaboration between computational linguists and computer scientists.

Research in smart homes is still quite recent; however, there is no doubt that it will become a pervasive research topic in the near future. This chapter analyzes the whole research production on smart homes indexed in the Scopus database from 1985 to 2019, yielding a total of 11,000 studies. One of the goals of this chapter is to identify the main countries and institutions that have published on this topic and what their interest has been throughout the time. Four out of 116 countries stand out in this field, namely China, USA, India, and South Korea. In terms of the main institutions, the three with the highest scientific output are Ulster University (UK), CNRS Centre National de la Recherche Scientifique (France), and Universite Grenoble Alpes (France). Another aim of the chapter is to determine the research fields and subfields investigated about smart homes. The publications are mainly focused on two scientific fields, that is, computer science and engineering, accounting for 64% of the overall scientific production. In an aggregate analysis of all publications, four main clusters have been identified, namely Internet of Things, Activity Recognition, Security, and Energy.

The problem of simplifying a complex shape with simpler ones is an important research area in computer science and engineering. In this paper we investigate the effect of visibility properties of polygons when their boundaries are approximated to have simpler shapes. The presented algorithm is expected to have wide applications in simplifying 1.5D terrain which are restricted class of simple polygons.

The aim of this paper is to develop a web application where scholars of the Faculty of Computer Science and Engineering (FINKI) at the University of Ss. Cyril and Methodius can display and share their projects and publications. Visitors can view, search through, and filter the authors, projects and publications that can be added and edited by the administrators via the administrator panel. In this paper, we first explain the type of system we are building and go through similar existing systems explaining how they work and what they offer. Then, we go through the programming languages and technologies we decided to use to develop this web application. After that, the development phase follows, where we describe each of the features we implemented. In The Final Product section we finally show images where you can see how the web application works and what it looks like. We finish the paper with a conclusion, briefly summarizing what we have achieved.

Fifty years after the publication of the first algorithms for the automatic detection of Atrial Fibrillation (AF), this cardiac condition is still the most studied from the computer science and engineering perspectives. Machine learning techniques are widely applied to a variety of problems, including detection, characterization, prediction and simulation, in general with promising results. In the last years, the Big Data + Deep Learning binomial is getting most of the attention in academia and industry, but on many occasions this approach fails on capitalizing all the knowledge acquired in previous decades of research. This article, written as a companion to the keynote with the same title presented in the CinC 2020 conference, tries to illustrate the importance of exploiting expert knowledge and classical approaches in synergy with the most advanced deep learning methods, which by themselves have fundamental limitations. The discussion is built around the AF detection problem and the conclusions extracted from the Physionet/CinC Challenge 2017, but the main points can be relevant in other problems for which humans have a better answer than computers, and this answer can be described.

A thesis report submitted to the School of Computer Science and Engineering, Nanyang Technological University, Singapore, in partial fulfilment of the requirements for the degree of Doctor of Philosophy.

A powerful new idea in the computational representation of structures is that of the digital twin. The concept of the digital twin emerged and developed over the last decade, and has been identified by many industries as a highly desired technology. The current situation is that individual companies often have their own definitions of a digital twin, and no clear consensus has emerged. In particular, there is no current mathematical formulation of a digital twin. A companion paper to the current one will attempt to present the essential components of the desired formulation. One of those components is identified as a rigorous representation theory of models; most importantly, governing how they are verified and validated, and how validation information can be transferred between models. Unlike its companion, which does not attempt detailed specification of any twin components, this paper will attempt to outline a rigorous representation theory of models, based on the introduction of two new concepts: mirrors and virtualizations. The paper is not intended as a passive wish list; it is intended as a rallying call. The new theory will require the active participation of researchers across a number of domains including: pure and applied mathematics, physics, computer science, and engineering. The paper outlines the main objects of the theory and gives examples of the sort of theorems and hypotheses that might be proved in the new framework.

This study aims to suggest a recommender System for undergraduate students who desire to seek admission into engineering courses in different Indian Institute of Technology (IITs) in India. Initially, the focus is to purpose a recommender system for admission into the top 10 IIT on a pilot basis in four common branches such as Electrical Engineering, Computer Science and Engineering, Mechanical Engineering, Civil Engineering. Data were collected from different authentic sources from 2016 to 2018. A model was built to predict the ranks for 2019 for each branch of every IITs. This paper illustrates prediction using Time Series Forecasting and recommendation algorithm using classification techniques. A comparative study of Random Forest Classification and K-Nearest Neighbor classification has been done. Finally, the recommendation algorithm shown reliable results with high accuracy in prediction model. It can be diversify and implement other streams as part of future work.

Cybersecurity education has been emphasized by several national organizations in the United States, including the National Academy of Engineering, which recognizes securing cyberspace as one of the 14 Engineering Grand Challenges. To prepare students for such challenges and to enhance cybersecurity education opportunities at our large research university, we implemented an NSF-funded cybersecurity education project. This project is a collaborative effort between faculty and graduate students in the Engineering Education, Computer Science (CS) and Computer Engineering (CPE) departments at a major US research university. In this effort, we integrated cybersecurity learning modules into multiple existing core CS and CPE courses following Jerome Bruner's spiral-theory model, which has previously been used to reformulate several academic curricula. In this paper, we present our cybersecurity curriculum initiative, describe the spiral-theory based process we developed to implement the curriculum and provide an in-depth description of four reusable cybersecurity learning modules that we developed. A core tenet of spiral theory holds to revisit topics as students advance through their curriculum. This work applies this approach to Cybersecurity education by carefully designing the learning objectives of the modules and its contents. For evaluating these learning modules we implemented pre and post-tests to assess students' technical knowledge, their perceptions towards the modules' learning objectives, and how it influenced their motivation to learn cybersecurity. Our findings are overwhelmingly positive and the students' feedback has helped us improve these learning modules. Since its inception, our initiative has educated more than $2\,000$ students and is currently being used to revise the affected courses' syllabi.

The development of technologies enables the application of the Internet of Things (IoT) in urban environments, creating smart cities. Hence, the optimal management of data generated in the interconnection of electronic sensors in real time improves the quality of life. The objective of this study is to analyze global research on smart cities based on IoT technology applications. For this, bibliometric techniques were applied to 1232 documents on this topic, corresponding to the period 2011&ndash, 2019, to obtain findings on scientific activity and the main thematic areas. Scientific production has increased annually, so that the last triennium has accumulated 83.23% of the publications. The most outstanding thematic areas were Computer Science and Engineering. Seven lines have been identified in the development of research on smart cities based on IoT applications. In addition, the study has detected seven new future research directions. The growing trend at the global level of scientific production shows the interest in developing aspects of smart cities based on IoT applications. This study contributes to the academic, scientific, and institutional discussion to improve decision making based on the available information.

In smart cities, the progress of technology has allowed the implementation of sensors, originating the Internet of Things (IoT) and making cities safer and more sustainable. Hence, the presence of elements that generate visual and artistic effects of IoT technology can make a great contribution to the provision of information that the urbanite needs. The aim of this study is to analyze worldwide research on the visual and artistic effects of IoT in smart cities. Bibliometric techniques were utilized on 1278 articles on this subject matter for the period of 2010&ndash, 2019 to achieve results on activity production. This has increased yearly, where in the last triennium, it has accumulated 85.21% of documents. Computer science and engineering were the most prominent subject areas where the articles were classified. The lines of research in the development of this research topic have been detected. Furthermore, the main directions for future research have also been identified. This study aims to contribute to highlighting the drivers of this field of research, in addition to providing the available information and future directions to improve academic and scientific discussion.

Due to its quantitative nature, bibliometrics is becoming increasingly popular among policy makers for academic hiring and career promotions. In this article, we quantitatively assess the impact that the granularity level in the classification of scientific areas would entail on research evaluation based on bibliometric indicators. We use as a case study the Italian national habilitation system (ASN), which classifies faculty members according to their academic discipline and relies on journal counts, citations, and h-indices as a basis for promoting tenure track researchers to associate professors and associate to full professors. The assessment checks whether the individual indicators of a researcher are above a certain threshold, e.g., the median over the population of researchers working in the same discipline. Our investigation focuses on two related, rather broad disciplines: computer science and computer engineering. We show that the ASN practice of using the same thresholds for all members of a scientific discipline can favor certain sub-communities that are characterized by higher bibliometric indicators, and disfavor others. We report evidence that up to 30% of Italian faculty members of certain sub-communities would see their indicators drop below the threshold, thus becoming not eligible for promotion, if the ASN were conducted on a more accurate, fine-grained classification. Conversely, in the same scenario, up to 11% of faculty members, in different sub-communities, would see their indicators rise above the threshold, granting them eligibility. Our data set includes 1685 authors, 89,185 distinct publications, and 262,286 author-publication pairs.

Film editing is a vital part in th2Department of Computer Science and Engineering, Toyohashi University of Technology, Tempaku, Toyohashi 441-8580, Japane post production process of a film. One of film technique is the montage editing technique, created by Sergei Eisenstein. Even so, Kuleshov’s “Effect” may be considered as the starting point of montage theory. Juxtaposition is a film editing technique that combines two or more shots to generate ideas or create thoughts. A montage can be a juxtaposition of two or more shots, but generally refers to a juxtaposition of several shots to describe a stretched event or a solidified time. This paper presents the experiment of juxtapositioning several shots in the scenes of a montage movie, In the Future, where the shots that have been sorted in the first version of this short film, then a few shots are exchanged positions in the second version then result a different story.

To address the computing challenge of ’big data’, a number of data-intensive computing frameworks (e.g., MapReduce, Dryad, Storm and Spark) have emerged and become popular. YARN is a de facto resource management platform that enables these frameworks running together in a shared system. However, we observe that, in cloud computing environment, the fair resource allocation policy implemented in YARN is not suitable because of its memoryless resource allocation fashion leading to violations of a number of good properties in shared computing systems. This paper attempts to address these problems for YARN. Both singlelevel and hierarchical resource allocations are considered. For single-level resource allocation, we propose a novel fair resource allocation mechanism called Long-Term Resource Fairness (LTRF) for such computing. For hierarchical resource allocation, we propose Hierarchical Long-Term Resource Fairness (H-LTRF) by extending LTRF. We show that both LTRF and H-LTRF can address these fairness problems of current resource allocation policy and are thus suitable for cloud computing. Finally, we have developed LTYARN by implementing LTRF and H-LTRF in YARN, and our experiments show that it leads to a better resource fairness than existing fair schedulers of YARN. Accepted version

Pattern recognition is a task of searching particular patterns or features in the given input. The data mining, computer networks, genetic engineering, chemical structure analysis, web services etc. are few rapidly growing applications where pattern recognition has been used. Graphs are very powerful model applied in various areas of computer science and engineering. This paper proposes a graph based algorithm for performing the graphical symbol recognition. In the proposed approach, a graph based filtering prior to the matching is performed which significantly reduces the computational complexity. The proposed algorithm is evaluated using a large number of input drawings and the simulation results show that the proposed algorithm outperforms the existing algorithms.

Teaching a statistics course for undergraduate computer science students can be very challenging: As statistics teachers we are usually faced with problems ranging from a complete disinterest in the subject to lack of basic knowledge in mathematics and anxiety for failing the exam, since statistics has the reputation of having high failure rates. In our case, we additionally struggle with difficulties in the timing of the lectures as well as often occurring absence of the students due to spare-time jobs or a long traveling time to the university. This paper reveals how these issues can be addressed by the introduction of a blended learning module in statistics. In the following, we describe an e-learning development process used to implement time- and location-independent learning in statistics. The study focuses on a six-step-approach for developing the blended learning module. In addition, the teaching framework for the blended module is presented, including suggestions for increasing the interest in learning the course. Furthermore, the first experimental in-class usage, including evaluation of the students’ expectations, has been completed and the outcome is discussed.

The gender gap in science, technology, engineering, and math (STEM) engagement is large and persistent. This gap is significantly larger in technological fields such as computer science and engineering than in math and science. Gender gaps begin early; young girls report less interest and self-efficacy in technology compared with boys in elementary school. In the current study (N=96), we assessed 6-year-old children's stereotypes about STEM fields and tested an intervention to develop girls' STEM motivation despite these stereotypes. First-grade children held stereotypes that boys were better than girls at robotics and programming but did not hold these stereotypes about math and science. Girls with stronger stereotypes about robotics and programming reported lower interest and self-efficacy in these domains. We experimentally tested whether positive experience with programming robots would lead to greater interest and self-efficacy among girls despite these stereotypes. Children were randomly assigned either to a treatment group that was given experience in programming a robot using a smartphone or to control groups (no activity or other activity). Girls given programming experience reported higher technology interest and self-efficacy compared with girls without this experience and did not exhibit a significant gender gap relative to boys' interest and self-efficacy. These findings show that children's views mirror current American cultural messages about who excels at computer science and engineering and show the benefit of providing young girls with chances to experience technological activities.

Parallel and distributed computing (PDC) is finding its usage from system, algorithms, and architecture perspectives in research and industries of many domains. Due to its ever-increasing applications and benefits, the need of skilled manpower in the area of PDC is also increasing. It is felt that if the basic knowledge taught through the core course of PDC is supplemented with the discussion of PDC topics in integration with other computer science courses, then it will not only provide the students with more opportunities to think in parallel, but will also motivate them to harness the best of PDC. In this paper, we present our experiences of performing research-oriented teaching of PDC topics in integration with other undergraduate courses since 2014 to 2016 spread over multiple semesters. The courses mainly include Software Engineering, Computer Networks, Computer Architecture, Network Programming, and Network based Laboratory taught to undergraduate level students of Computer Science and Engineering. Our integration plan is encouraged from the objectives of NSF/TCPPIEEE core curriculum initiative on PDC. Most of these courses are compulsory courses for undergraduate students of our department. In addition to the goals of the respective courses, we tried to fulfill many pedagogical goals corresponding to the PDC course. The teaching-contents of these courses have been adapted to also cover the aspects of PDC. A well-defined methodology for selection of PDC topics for integration, selection of topics for laboratory projects and home assignments, conduct of examinations (mid-term/end-term), pre-post feedback and evaluation, and other related activities has been planned. The methodology is intended to fulfill the priorly set goals and to achieve the intended learning outcomes (ILOs). This paper presents the methodology used, detailed topics and integration plan of PDC topics along with corresponding bloom levels, ILOs, evaluation strategies, and performance evaluation based on the students feedback and statistical analysis. Success of integration has been validated by performing statistical analysis of students pre-post feedback and performance in examinations. An integration plan for research-oriented teaching of relevant topics from PDC.The work is encouraged from the IEEE/TCPP Core curriculum initiative on PDC.Software Engineering, Computer Networks, Computer Architecture under integration.Validation by statistical analysis of pre-post feedback and students performance.

Counts of Mendeley readers may give useful evidence about the impact of published research. Although previous studies have found significant positive correlations between counts of Mendeley readers and citation counts for journal articles, it is not known if this is equally true for conference papers. To fill this gap, Mendeley readership data and Scopus citation counts were extracted for both journal articles and conference papers published in 2011 in four fields for which conferences are important: Computer Science Applications; Computer Software; Building and Construction Engineering; and Industrial and Manufacturing Engineering. Mendeley readership counts correlated moderately with citation counts for both journal articles and conference papers in Computer Science Applications and Computer Software. The correlations were much lower between Mendeley readers and citation counts for conference papers than for journal articles in Building & Construction Engineering and Industrial and Manufacturing Engineering. Hence, there seem to be disciplinary differences in the usefulness of Mendeley readership counts as impact indicators for conference papers, even between fields for which conferences are important.

Women are vastly underrepresented in the fields of computer science and engineering (CS&E). We examined whether women might view the intellectual characteristics of prototypical individuals in CS&E in more stereotype-consistent ways than men might and, consequently, show less interest in CS&E. We asked 269 U.S. college students (187, 69.5% women) to describe the prototypical computer scientist (Study 1) or engineer (Study 2) through open-ended descriptions as well as through a set of trait ratings. Participants also rated themselves on the same set of traits and rated their similarity to the prototype. Finally, participants in both studies were asked to describe their likelihood of pursuing future college courses and careers in computer science (Study 1) or engineering (Study 2). Across both studies, we found that women offered more stereotype-consistent ratings than did men of the intellectual characteristics of prototypes in CS (Study 1) and engineering (Study 2). Women also perceived themselves as less similar to the prototype than men did. Further, the observed gender differences in prototype perceptions mediated the tendency for women to report lower interest in CS&E fields relative to men. Our work highlights the importance of prototype perceptions for understanding the gender gap in CS&E and suggests avenues for interventions that may increase women’s representation in these vital fields. Electronic supplementary material The online version of this article (doi:10.1007/s11199-017-0763-x) contains supplementary material, which is available to authorized users.

Instructional strategies for e-content development have to be entirely different from that for traditional printed materials, such as text books and manuals. This is due to the fact that, technically and socially both have different instructional processes. Due to the advent of ICT development in recent times, many Computer Technology subject contents are being uploaded in e-learning modes. Training imparted through e-modes has increased in recent times specifically in India. This paper demonstrates the effectiveness of our suggestion on Real World Problem centric Instructional approach. Specific issues on the e-mode have been inferred through a social study conducted on Indian learners (respondents). The paper justifies the adaptation of ‘First Principles of Instruction’ through our experimental study. Due to reasons elaborated in the paper this proven Instructional approach using ‘First Principles of Instruction’ would be well suited for e-content development of Computer Science and Engineering subjects.

In recent decade, a wave of apprehension swept across the Pearl of Oriental with regards to the disappearance of high value tangible and intangible heritages due to urban renewal and redevelopment. The flourish of computer science and engineering, however, offers a rosy spectacle for heritage preservation. Virtual heritage is no longer an idea in cloud-cuckoo-land. In this research paper, we propose the ground breaking five dimensional GIS virtual heritage. Whilst traditional two dimensional GIS stores the information of X and Y axis, the recent three dimensional GIS includes the information of building height and other information is useful to urban planners when they make decision in change in use of land. The fourth dimension includes time with collective memories of Hong Kongers. The invaluable intangible heritage can be kept by record of oral history. Changes in ethnic minorities, different walks of lives provide dazzling and interesting angles of the modern city. We also include the concept of the fifth dimension of automated virtual and augmented heritage with drone and robots’ help in taking 360 degree virtual reality videos and photos. Whilst strand of literatures on VR, AR, 3D GIS and memories scatter in different areas of research, combination of all these technologies is the first of its kind. Research on automated VR and AR is scarce or non-exist. It is expected that the 5D GIS virtual heritage offers a new angle to historian, economists, sociologists, urban planners.

Este artigo apresenta o Laboratório de Interação Flexível e Sustentável (LIFeS). Este laboratório foi fundado em 2010 e está hospedado no Departamento de Computação da Universidade Federal de São Carlos. O objetivo principal do laboratório é avançar o estado da arte em Ciência e Engenharia da Computação, especialmente nos campos da Interação Homem-Computador. A pesquisa centra-se na identificação da diversidade dos usuários, incluindo aspectos emocionais, e no projeto e avaliação de soluções de interface de usuário flexíveis para vários dispositivos, plataformas e contextos como web, ubíquo e móvel. Mais recentemente, o laboratório vem investigando como projetar aplicações terapêuticas. Também se investiga como levar a sustentabilidade ao design e como os designers podem favorecer a sustentabilidade por meio de soluções computacionais.

A powerful new idea in the computational representation of structures is that of the digital twin. The concept of the digital twin emerged and developed over the last two decades, and has been identified by many industries as a highly-desired technology. The current situation is that individual companies often have their own definitions of a digital twin, and no clear consensus has emerged. In particular, there is no current mathematical formulation of a digital twin. A companion paper to the current one will attempt to present the essential components of the desired formulation. One of those components is identified as a rigorous representation theory of models, how they are validated, and how validation information can be transferred between models. The current paper will outline the basic ingredients of such a theory, based on the introduction of two new concepts: mirrors and virtualisations. The paper is not intended as a passive wish-list; it is intended as a rallying call. The new theory will require the active participation of researchers across a number of domains including: pure and applied mathematics, physics, computer science and engineering. The paper outlines the main objects of the theory and gives examples of the sort of theorems and hypotheses that might be proved in the new framework.

Multiple studies have shown that gender balance in the fields of Science, Technology, Engineering and Maths -- and in particular in ICT -- is still far to be achieved. Several initiatives have been recently taken to increase the women participation, but it is difficult, at present, to evaluate their impact and their potential of changing the situation. This paper contributes to the discussion by presenting a descriptive analysis of the gender balance in Computer Science and Computer Engineering in Italian Universities.

The Expert Systems With Applications (ESWA) is a leading journal in the fields of computer science and engineering. Motivated by its outstanding performance, this paper seeks to develop a comprehensive overview of research activities in ESWA since its inception in 1990. In this regard, bibliometric techniques have been utilized to characterize the status quo, dynamics, and development trends of research patterns in ESWA. In doing so, the work used Scopus database as a source of data required. A data visualization software, visualization of similarities (VOS) viewer, was used to map the bibliographic material. The Scopus database yielded 12,254 documents published in ESWA from 105 countries with an average of 408 documents/year. The most productive country was Taiwan (2,069 documents; 17.0%). National Cheng Kung University, Taiwan, was the most productive institution (219 documents; 1.8%). The major topics which will continue to be active include genetic algorithms, data mining, neural networks, support vector machines, classification and machine learning, feature selection, particle swarm optimization, fuzzy logic, and clustering. The outcomes underline the significant growth of ESWA through time. The vitality of topics addressed in ESWA to solve real-world problems boosts the progress and advancements of knowledge in this journal.

Using the National Science Foundation’s SESTAT data, we examine the gender wage gap by race among those working in computer science, life sciences, physical sciences, and engineering. We find that in fields with a greater representation of women (the life and physical sciences), the gender wage gap can largely be explained by differences in observed characteristics between men and women working in those fields. In the fields with the lowest concentration of women (computer science and engineering), gender wage gaps persist even after controlling for observed characteristics. In assessing how this gap changes over time, we find evidence of a narrowing for more recent cohorts of college graduates in the life sciences and engineering. The computer sciences and physical sciences, however, show no clear pattern in the gap across cohorts of graduates.

Supervised by Dr. Abu Raihan Mostofa Kamal, Associate Professor, Department of Computer Science and Engineering, Islamic University of Technology