Computational Models for the Analysis of Attention

Assalamu’alaikum Wr. Wb. and Greetings.I am pleased and honored to welcome you at the First International Conference on Computer Science and Applied Mathematic (ICCSAM 2018), which was held this year by AMIK and STIKOM Tunas Bangsa Pematangsiantar in collaboration with the Indonesian Computer and Information Professional Association (IPKIN), Indonesian Mathematical Society (INDOMS) and Bank Muamalat. As Chair of the Foundation, I would like to convey a greeting Welcome to Parapat City Lake Toba to presenters from various countries and especially those I respect for the presence of scientists in the field of Computer and Mathematical Sciences and industry practitioners in the 2018 ICCSAM event.I see that this activity is designed to enhance the exchange of knowledge and new discoveries in computer science and mathematics and related fields in industry. I hope that scientists in the field of computer science and mathematics and those who work in the industry can share knowledge and work together as a team that has mutual relations with each other. I am very pleased to say that the theme of this conference, “Advancing Computability Innovation” is very much in line with the objectives of AMIK and STIKOM conferences and missions, namely “to become a Science and Technology based Study Program that meets industrial needs and functions as a research center in Information Data Science. The conference was held in response to output and aoutcame which had a significant contribution in the field of computer science and mathematics as an inseparable unit of science towards the development of local and global industries. It is our happiness and honor to welcome distinguished professors present to convey their expertise at this conference.I hope this meeting will enable the development of productive dialogue between participants from various countries. They also provide invaluable opportunities for networking among participants, institutions and industries. I also hope that diversity in these fields can reveal more opportunities for researchers and practitioners from all over the world to start a lot of research related to the industry in the future.I would like to congratulate AMIK and STIKOM Tunas Bangsa for starting this conference with a synergistic contribution from dedicated partners namely the USU Faculty of Mathematics and Natural Sciences, USU’s Faculty of Computer and Information Technology, the Indonesian Computer and Informatics Professional Association (IPKIN), Indonesian Mathematical Society (INDOMS), Bank Muamalat, Malaysia Pahang University (UMP), University of Essex, United Kingdom, Ankara, Turkey and Institute of Applied Mathematics Middle East Technical University to all delegates for their full support, cooperation and contribution to the 2018 ICCSAM. I also want to thank the Organizing Committee for their perseverance and extraordinary efforts. Various sponsors are also rewarded for their contributions. I really hope that all participants will get a pleasant stay here at NIAGARA HOTEL and bring back unforgettable experiences and valuable knowledge from this conference.Thanks.H. Ahmad Ridwansyah Putra

Despite great effort in research and teaching as well as in raising women’s quotas in the public sector and in enterprises, a gender gap in computer science can still be observed. The reasons are manifold, including the lack of interest of girls because of existing stereotypes of male nerds, misconceptions in the field of computer science, and concrete differences in students’ self-concept and performance. Girls do not choose schools or studies in the field of computer science because working on computers or coding is unattractive to them. Research shows poorer performance, missing interest, and lower self-concept for girls in secondary computing education as well as a high dropout rate of female students in computer science courses at the bachelor’s level. Software development exams contribute to those high dropout rates, and women often choose a different subject or even stop their university education altogether. This leads to fewer female employees in the field of computer science, and this situation calls for special measures at several levels. The current paper provides an overview of the initiatives of our university that aim to address several aspects of the gender gap. The programs offered aim to (1) increase interest in computer science and recruit (highly) gifted girls for our talent programs, (2) recruit and support female bachelor’s and master’s students in computer science, and (3) redesign programming courses and teaching materials to reduce the gender gap in performance and support, especially for female students. This study also describes a cyber tutoring program in which highly gifted girls between 13 and 16 years collaborate with and are supervised by female role models in higher positions in STEM. The evaluation results gained in the first year of this case study are promising and suggest that the extension and further development of the program would be advantageous.

Cognitive informatics (CI), a multidisciplinary area of research tries to solve the common problems in the field of informatics, computer science, software engineering, mathematics, cognitive science, neurobiology, psychology, and physiology. Measurement in software engineering is also a core issue which is still striving for its standardization process. In recent years, several cognitive complexity measures based on CI have been proposed. However, each of them has their own advantages and disadvantages. This chapter presents a critical review on existing cognitive complexity measures. Furthermore, a comparative study based on some selected attributes has been presented.

The artificial neural network began in 1940, which is an important part of artificial intelligence. At present, it has become a hot topic in the fields of neuroscience, computer science, brain science, mathematics, and psychology. Thomas Bayes firstly reported the Bayesian theory in 1763. After the development in the twentieth century, it has been widespread in all areas of statistics. In recent years, due to the solution of the problem of high-dimensional integral calculation, Bayesian Statistics has been improved theoretically, which solved many problems that cannot be solved by classical statistics and is also applied to the interdisciplinary fields. In this paper, the related concepts and principles of the artificial neural network are introduced. It also summarizes the basic content and principle of Bayesian Statistics, and combines the artificial neural network technology and Bayesian decision theory and implement them in all aspects of image recognition, such as enhanced face detection method based on neural network and Bayesian decision, as well as the image classification based on the Bayesian decision. It can be seen that the combination of artificial intelligence and statistical algorithms has always been the hot research topic.

Veiled Figures: Pioneering Women Religious in the Sciences Jennifer A Head Traditionally, the term ‘Catholic Sister’ has conjured an image of either a cloistered woman in contemplative prayer or a woman in a habit, working as a nurse or teacher. Today that image has expanded to a woman wearing a cross around her neck and working in pastoral care, parish ministry or social justice movements. Rarely, if ever, does this image include a woman at the forefront of computer science, working with plutonium or providing flight instruction to future military pilots. This article explores some of the ‘hidden history’ of Catholic women religious in science.1 This hidden history of women religious includes the first female pharmacist in America, the creator of the first reasonably priced and easily constructed incubator for premature babies and one of the first women to receive a PhD in the field of computer science. Sisters were also pioneers in the fields of zoology, cytogenetics, astrophysics and the treatment of polio. In many cases, they were the first women granted advanced degrees from institutions previously only open to men. Many of these women were also strong advocates for the inclusion of women in the sciences – one even permitted her students to bring their children to class, if childcare was not available. The first two sisters I encountered were both Sisters of Charity of the Blessed Virgin Mary (BVMs): Sr Mary Kenneth Keller (1913–85) and Sr Mary Therese Langerbeck (1902–93). Mary Kenneth Keller (1913–85), computer scientist Sister Mary Kenneth Keller was among the first women to receive a PhD in computer science. She received her degree from the University of WisconsinMadison in 1964 and was the first person to receive such a degree from that educational institution. Sr Kenneth first became interested in the field of computer science while studying for a Master’s degree in Mathematics at DePaul University in the early 1950s. In 1958, she applied for a six-month National Science Foundation workshop at Dartmouth University’s Computer Veiled Figures: Pioneering Women Religious in the Sciences Studies • volume 107 • number 427 293 Center. She was the first woman permitted to work in the computer centre. As part of this workshop, she assisted in the development of BASIC – the first ‘easy to learn’ computer language. After receiving her PhD, she was appointed chair of the newly created computer science department at Clarke College (now Clarke University), a position she would hold for almost twenty years. At the time, Clarke was a women’s college, though it has since gone ‘co-ed’. Under Sr Kenneth’s leadership, the programme grew to include a master’s degree programme in Computer Applications in Education. The programme was also open to students from nearby Loras College, as well as to teachers, local business owners, bankers and others for whom computer use was beginning to be a workplace requirement. Sr Kenneth was among the first to recognise the future importance of computers, noting in 1964 that students in such fields as education, psychology and the sciences were already finding uses for computers in their academic pursuits. She and other members of the computer science faculty and staff wrote programmes to assist academic departments on campus. Chemistry students ran simulated experiments while psychology students used a programme to run a hypothetical ‘stat rat’ through a learning maze, saving time ‘both ours and the rat’s!’2 She also anticipated the importance computers would have in libraries, recognising that ‘its function in information retrieval will make it the hub of tomorrow’s libraries’.3 She believed that using a computer required two virtues: humility and patience. Humility, because the computer did not make mistakes – the programmer did. Patience was required in ‘de-bugging’ the programme and determining which lines of code created the mistakes.4 In addition to her responsibilities as a Clarke faculty member, Sr Kenneth authored four books in her field. She also served as a consultant for various governmental and civic groups including the State of Illinois and the City of Dubuque, Iowa. A fixture in the early ‘IT’ world of the Upper Mississippi River Valley, she once commented that ‘around Dubuque nobody buys a computer...

Today more than ever, the importance of technology is increasing rapidly and related job opportunities are growing in electrical engineering, computer science and other related fields. Despite this trend, female employment in these fields is still low and it is hard to find renowned female scientists and programmers. In addition, most attendees at technology exhibitions and readers of technology magazines are male. Why aren't women interested in computer science and electrical engineering? I believe that creating an interest in technology and science for women from a young age could help increase the number of females in technology-based fields. When a mother and daughter make electronic crafts together, the mother becomes a good role model for technology use, and can help stimulate her child's interests in science and technology. This paper explores the reasons why women are not interested in technology and how this can be overcome.

Emergence of relational reasoning

Since the birth of the subject of artificial intelligence, the long-term goal of this subject is to realize the intelligent system of human level. However, after nearly 60 years of development, no general intelligent system has been able to approach the level of human beings: it has the ability to cooperate with a variety of different cognitive abilities; it has a strong adaptive ability to complex environments; it has the ability of self-learning to new things and new environments. With the development of Brain and Neuroscience and Cognitive Science, it is possible to observe some activities of Brain Neural Networks under various cognitive tasks at different scales and obtain relevant data. Therefore, inspired by brain working mechanism, the development of Brain-like Intelligence has become a hotspot in the field of Artificial Intelligence and Computational Science in recent years. Machine Intelligence Brain-like Intelligence System (MIBS) inspired by cognitive behavior mechanism and realized by hardware and software cooperation is similar to human beings in information processing mechanism, cognitive behavior and intelligence level. The goal is to enable machines to realize various human cognitive abilities and their coordination mechanism, and ultimately reach or surpass the level of human intelligence. In this paper, we will study from the perspective of brain science, cognitive science and artificial intelligence. This paper reviews the history, current situation and research focus of brain-like intelligence research, and looks forward to its development direction, potential application fields and potential far-reaching impact.

Though there is an increasing need for computer scientists in our society, gifted young people with strong mathematical background, who would be well-suited for a study of computer science, often do not consider this choice because they have a wrong picture of computer science in mind. In this paper we present a new concept of collaborative learning to introduce high-school students to the field of theoretical computer science. In particular, we choose the demanding field of real-time systems in order to illustrate the applicability of theoretical methods on real-world systems. Statistical results show that with our concept we are able to point out the manifoldness, beauty and challenge of this scientific area and can convince high-school students to consider computer science as a choice of study.

In this era and in the future, artificially intelligent machines are replacing and playing a key role to enhance human capabilities in many areas. It is also making life style better by providing convenience to all including normal human beings and professionals as well. That is why AI is gaining huge attention and popularity in the field of computer science by which it has revolutionized the rapidly growing technology known as expert system. The applications of AI are working in many areas with huge impact and being used widely as well. AI provides quality and efficiency in almost every area, we are evolving it in. The main purpose of this paper is to explore the area of medical and health-care with respect to AI along with ‘Machine Learning’, and ‘Neural Networks’. This work explores the current use of AI in innovations, in the particular field of Bio-Medical and evaluated that how it has improved hospital inpatient care and other sectors related to it i.e. smart medical home, virtual presence of doctors and patients, automation in diagnostic, etc. that has changed the infrastructure of medical domain. Finally, an investigation of some expert systems and applications is made. These systems and applications are widely used throughout the world and a ranking mechanism of their performance has proposed accordingly in an organized manner. We hope, this work will be helpful for the researchers coming to this particular area and to provide a syntactic information that how computer science (i.e. AI, ANN, ML) is revolutionizing the field of bio-medical and healthcare.

Survey on synchronization mechanisms in machine-to-machine systems

The question is, How does the brain make its mind? In Cognition, computation and consciousness [Ito et al. (Eds) (1997) Oxford & New York: Oxford University Press], a variety of noted theoreticians from the fields of cognitive psychology, computer science, and philosophy postulate answer-blueprints rather than full-blown explanatory solutions to this most nettlesome question. Coming to the problem from quite different starting points and perspectives, they nevertheless succeed in reaching consensus on the idea that the contingencies of the brain's evolution have resulted in an organ that generates its mind by a complex process of information exchange among its constituents. Put in the vernacular, the brain produces its mind by having its parts, especially those most recently evolved, talk to each other. In this essay I take a critical look at proposals of several celebrated (neuro)scientists and philosophers in their specific areas of expertise. The underlying theme of brain component communication suggests the image of conversations in the cortex. From such cortical conversations arise selves (the mind/brain's I) and their stories and projects. This in turn suggests the idea that the brain is a stage where a Pirandello-like play is continually rehearsed.

Reviewed by: The analogical mind: Perspectives from cognitive science ed. by Dedre Gentner, Keith J. Holyoak, and Boicho N. Kokinov Rick DeNoble The analogical mind: Perspectives from cognitive science. Ed. by Dedre Gentner, Keith J. Holyoak, and Boicho N. Kokinov. Cambridge, MA: MIT Press, 2001. Pp. 541. ISBN 0262571390. $28.35. This book grew out of a 1998 summer workshop at the New Bulgarian University in Sofia, Bulgaria [End Page 456] called ‘Advances in Analogy Research: Integration of Theory and Data from the Cognitive, Computational, and Neural Sciences’. While the contributors come from a variety of disciplines, they share an interest in a common approach to cognitive studies. The book consists of an introduction by the volume editors, followed by thirteen chapters divided into three sections: ‘Computational and theoretical approaches’, ‘Arenas of analogical thought’, and ‘Developmental approaches’. An epilogue concludes the volume. The introduction provides a brief history of the development of the study of analogy within cognitive science, a very brief statement of the current framework for analogy studies, and an outline of the remainder of the book’s contents. Section 1 consists of four chapters based on actual running computational models of analogical thinking. These include: ‘Exploring analogy in the large’ (23–50) by Kenneth D. Forbus; ‘Integrating meaning and reasoning in analogy making: The AMBR model’ (54–124) by Boicho N. Kokinov and Alexander A. Petrov; ‘The STAR-2 model for mapping hierarchically structured analogs’ (125–60) by William H. Wilson, Graeme S. Halford, Brett Gray, and Steven Phillips; and ‘Toward an understanding of analogy within a biological symbol system’ (161–95) by Keith J. Holyoak and John E. Hummel. Section 2 contains seven chapters, which deal with the role analogy plays within a variety of complex cognitive processes or tasks. The first three deal especially with analogical processes in language. The chapters in Section 2 include: ‘Metaphor is like analogy’ (199–254) by Dedre Gentner, Brian Bowdle, Phillip Wolff, and Consuelo Boronet; ‘Conceptual blending and analogy’ (255–86) by Gilles Fauconnier; ‘Setting limits on analogy: Why conceptual combination is not structural alignment’ (287–312) by Mark T. Keane and Fintan Costello; ‘The analogical paradox: Why analogy is so easy in naturalistic settings yet so difficult in the psychological laboratory’ (313–34) by Kevin Dunbar; ‘Emotional analogies and analogical inference’ (335–62) by Paul Thagard and Cameron Shelley; ‘Analogy and analogical comparison in choice’ (363–400) by Arthur B. Markman and C. Page Moreau; and ‘Semantic alignments in mathematical word problems’ (401–33) by Miriam Basok. The last section contains two chapters: ‘Analogical reasoning in children’ (437–70) by Usha Goswami, and ‘Can an ape reason analogically? Comprehension and production of analogical problems by Sarah, a chimpanzee (Pan troglodytes)’ (471–97) by David L. Oden, Roger K. R. Thompson, and David Premack. The epilogue is written by Douglas Hofstadter, who claims in a supposedly unorthodox view that, rather than being a ‘blip in the broad blue sky’, analogy is actually ‘the very blue that fills the whole sky of cognition—analogy is everything, or very nearly so, in my view’ (499). The essays in the book go a long way toward making this claim the standard view of cognitive science. Rick DeNoble Boulder, CO Copyright © 2006 Linguistic Society of America

Exploration of a new integrative intellectual enterprise: the cognitive social sciences. Research in the cognitive sciences has advanced significantly in recent decades. Computational cognitive modeling has profoundly changed the ways in which we understand cognition. Empirical research has progressed as well, offering new insights into many psychological phenomena. This book investigates the possibility of exploiting the successes of the cognitive sciences to establish a better foundation for the social sciences, including the disciplines of sociology, anthropology, economics, and political science. The result may be a new, powerful, integrative intellectual enterprise: the cognitive social sciences. The book treats a range of topics selected to capture issues that arise across the social sciences, covering computational, empirical, and theoretical approaches. The chapters, by leading scholars in both the cognitive and the social sciences, explore the relationship between cognition and society, including such issues as methodologies of studying cultural differences; the psychological basis of politics (for instance, the role of emotion and the psychology of moral choices); cognitive dimensions of religion; cognitive approaches to economics; meta-theoretical questions on the possibility of the unification of social and cognitive sciences. Combining depth and breadth, the book encourages fruitful interdisciplinary interaction across many fields.

Book synopsis: Over the past century, educational psychologists and researchers have posited many theories to explain how individuals learn, i.e. how they acquire, organize and deploy knowledge and skills. The 20th century can be considered the century of psychology on learning and related fields of interest (such as motivation, cognition, metacognition etc.) and it is fascinating to see the various mainstreams of learning, remembered and forgotten over the 20th century and note that basic assumptions of early theories survived several paradigm shifts of psychology and epistemology. Beyond folk psychology and its naive theories of learning, psychological learning theories can be grouped into some basic categories, such as behaviorist learning theories, connectionist learning theories, cognitive learning theories, constructivist learning theories, and social learning theories. Learning theories are not limited to psychology and related fields of interest but rather we can find the topic of learning in various disciplines, such as philosophy and epistemology, education, information science, biology, and – as a result of the emergence of computer technologies – especially also in the field of computer sciences and artificial intelligence. As a consequence, machine learning struck a chord in the 1980s and became an important field of the learning sciences in general. As the learning sciences became more specialized and complex, the various fields of interest were widely spread and separated from each other; as a consequence, even presently, there is no comprehensive overview of the sciences of learning or the central theoretical concepts and vocabulary on which researchers rely. The Encyclopedia of the Sciences of Learning provides an up-to-date, broad and authoritative coverage of the specific terms mostly used in the sciences of learning and its related fields, including relevant areas of instruction, pedagogy, cognitive sciences, and especially machine learning and knowledge engineering. This modern compendium will be an indispensable source of information for scientists, educators, engineers, and technical staff active in all fields of learning. More specifically, the Encyclopedia provides fast access to the most relevant theoretical terms provides up-to-date, broad and authoritative coverage of the most important theories within the various fields of the learning sciences and adjacent sciences and communication technologies; supplies clear and precise explanations of the theoretical terms, cross-references to related entries and up-to-date references to important research and publications. The Encyclopedia also contains biographical entries of individuals who have substantially contributed to the sciences of learning; the entries are written by a distinguished panel of researchers in the various fields of the learning sciences.