by Cheryl Ann Wheeler.; Thesis (B.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1982.; MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING; Includes bibliographical references.

Making software reliable is one of the most important technological challenges facing our society today. This thesis presents a new type system that addresses this problem by statically preventing several important classes of programming errors. If a program type checks, we guarantee at compile time that the program does not contain any of those errors. We designed our type system in the context of a Java-like object-oriented language; we call the resulting system SafeJava. The SafeJava type system offers significant software engineering benefits. Specifically, it provides a statically enforceable way of specifying object encapsulation and enables local reasoning about program correctness; it combines effects clauses with encapsulation to enable modular checking of methods in the presence of subtyping; it statically prevents data races and deadlocks in multithreaded programs, which are known to be some of the most difficult programming errors to detect, reproduce, and eliminate; it enables software upgrades in persistent object stores to be defined modularly and implemented efficiently; it statically ensures memory safety in programs that manage their own memory using regions; and it also statically ensures that real-time threads in real-time programs are not interrupted for unbounded amounts of time because of garbage collection pauses. Moreover...

As the world becomes increasingly connected, traditional problem solving and decision-making skills becomes less effective. Complex systems found in nature and society exhibit long time delays between cause and effect, feedback, and non-linearity making it difficult to reason effectively about system behavior. Recent studies have shown even highly educated graduate students lack basic systems thinking skills indicating a need for improved system dynamics education. This paper describes the development of a two new tools for improving system dynamics education: a stock-flow simulator that allows users to experiment with simple stock-flow systems, and a web application framework for building system dynamics surveys. This framework is used to build a survey capable of evaluating systems thinking skills and compare the effectiveness of online teaching methods.; by Nicholas A. Behrens.; Thesis (M. Eng. and S.B.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.; Includes bibliographical references (leaf 50).

This ethnography tracks visualization and pedagogy in the burgeoning field of structural biology. Structural biologists are a multidisciplinary group of researchers who produce models and animations of protein molecules using three-dimensional interactive computer graphics. As they ramp up the pace of structure determination, modeling a vast array of proteins, these researchers are shifting life science research agendas from decoding genetic sequence data to interpreting the multidimensional forms of molecular life. One major hurdle they face is training a new generation of scientists to work with multi-dimensional data forms. In this study I document the formation and propagation of tacit knowledge in structural biology laboratories, in classrooms, and at conferences. This research shows that structural biologists-in-training must cultivate a feel for proteins in order to visualize and interpret their activity in cells. I find that protein modeling relies heavily on a set of practices I call the body-work of modeling. These tacit skills include: a) forms of kinesthetic knowledge that structural biologists gain through building and manipulating molecular models, and by using their own bodies as mimetic models to help them figure out how proteins move and interact; and b) narrative strategies that assume a teleological relationship between form and function...

This dissertation recounts the development of graphic models of human bodies and virtual reality simulators for teaching anatomy and surgery to medical students, residents, and physicians. It considers how researchers from disciplinary cultures in medicine, engineering, and computer programming come together to build these technologies, bringing with them values and assumptions about bodies from each of their disciplines, values and assumptions that must be negotiated and that often are made material and embedded in these new technologies. It discusses how the technological objects being created privilege the body as a dynamic and interactive system, in contrast to the description and taxonomic body of traditional anatomy and medicine. It describes the ways that these technologies create new sensory means of knowing bodies. And it discusses the larger cultural values that these technologies reify or challenge. The methodology of this dissertation is ethnography. I consider in-depth one laboratory at a major medical school, as well as other laboratories and researchers in the field of virtual medicine. I study actors in the emerging field of virtual medicine as they work in laboratories, at conferences, and in collaborations with one another. I consider the social formations that are developing with this new discipline. Methods include participant observation of laboratory activities...

This course will study the development of modern science from the seventeenth century to the present, focusing on Europe and the United States. It will not focus on discoveries and their discoverers. Instead, it will examine: What is science? How has science been practiced, and by whom? How are discoveries made and accepted? What is the nature of scientific progress? What is the impact of science and society? What is the impact of society on science? Topics will be drawn from the histories of physics, chemistry, biology, geology, medicine, psychology, and computer science.

Social media is becoming increasingly important in society and culture, empowering consumers to group together on common interests and share opinions through the Internet. The social web shifts the originators of content from companies to users. Differences caused by this dynamic result in existing web analytic techniques being inadequate. Because people reveal their thoughts and preferences in social media, there are significant opportunities in business intelligence by analyzing social media. These opportunities include brand monitoring; trend recognition, and targeted advertising. The market for social media analytics in business intelligence is further validated by its direct application in the consumer research market. Challenges lie ahead for development and adoption of social media analytics. Technology used in these analytics, such as natural language processing and social network analysis, need to mature to improve accuracy, performance, and scalability. Nevertheless, social media continues to grow at a rapid pace, and organizations should form strategies to incorporate social media analytics into their business intelligence frameworks.; by Bobby Lo.; Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science...

Privacy laws are an important facet of our society. But they can also serve as formidable barriers to medical research. The same laws that prevent casual disclosure of medical data have also made it difficult for researchers to access the information they need to conduct research into the causes of disease. But it is possible to overcome some of these legal barriers through technology. The US law known as HIPAA, for example, allows medical records to be released to researchers without patient consent if the records are provably anonymized prior to their disclosure. It is not enough for records to be seemingly anonymous. For example, one researcher estimates that 87.1% of the US population can be uniquely identified by the combination of their zip, gender, and date of birth - fields that most people would consider anonymous. One promising technique for provably anonymizing records is called k-anonymity. It modifies each record so that it matches k other individuals in a population - where k is an arbitrary parameter. This is achieved by, for example, changing specific information such as a date of birth, to a less specific counterpart such as a year of birth.; (cont.) Previous studies have shown that achieving k-anonymity while minimizing information loss is an NP-hard problem; thus a brute force search is out of the question for most real world data sets. In this thesis...

Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science. In most of the aforementioned cases graphs are directed - in the sense that there is directionality on the edges, making the semantics of the edges non symmetric. An interesting feature that real networks present is the clustering or community structure property, under which the graph topology is organized into modules commonly called communities or clusters. The essence here is that nodes of the same community are highly similar while on the contrary, nodes across communities present low similarity. Revealing the underlying community structure of directed complex networks has become a crucial and interdisciplinary topic with a plethora of applications. Therefore, naturally there is a recent wealth of research production in the area of mining directed graphs - with clustering being the primary method and tool for community detection and evaluation. The goal of this paper is to offer an in-depth review of the methods presented so far for clustering directed networks along with the relevant necessary methodological background and also related applications. The survey commences by offering a concise review of the fundamental concepts and methodological base on which graph clustering algorithms capitalize on. Then we present the relevant work along two orthogonal classifications. The first one is mostly concerned with the methodological principles of the clustering algorithms...

Co-authorship in publications within a discipline uncovers interesting properties of the analysed field. We represent collaboration in academic papers of computer science in terms of differently grained networks, including those sub-networks that emerge from conference and journal co-authorship only. We take advantage of the network science paraphernalia to take a picture of computer science collaboration including all papers published in the field since 1936. We investigate typical bibliometric properties like scientific productivity of authors and collaboration level in papers, as well as large-scale network properties like reachability and average separation distance among scholars, distribution of the number of scholar collaborators, network resilience and dependence on star collaborators, network clustering, and network assortativity by number of collaborators.

Most of the existing information retrieval systems are based on bag of words model and are not equipped with common world knowledge. Work has been done towards improving the efficiency of such systems by using intelligent algorithms to generate search queries, however, not much research has been done in the direction of incorporating human-and-society level knowledge in the queries. This paper is one of the first attempts where such information is incorporated into the search queries using Wikipedia semantics. The paper presents an essential shift from conventional token based queries to concept based queries, leading to an enhanced efficiency of information retrieval systems. To efficiently handle the automated query learning problem, we propose Wikipedia-based Evolutionary Semantics (Wiki-ES) framework where concept based queries are learnt using a co-evolving evolutionary procedure. Learning concept based queries using an intelligent evolutionary procedure yields significant improvement in performance which is shown through an extensive study using Reuters newswire documents. Comparison of the proposed framework is performed with other information retrieval systems. Concept based approach has also been implemented on other information retrieval systems to justify the effectiveness of a transition from token based queries to concept based queries.; Comment: 44 pages

We represent collaboration of authors in computer science papers in terms of both affiliation and collaboration networks and observe how these networks evolved over time since 1960. We investigate the temporal evolution of bibliometric properties, like size of the discipline, productivity of scholars, and collaboration level in papers, as well as of large-scale network properties, like reachability and average separation distance among scientists, distribution of the number of scholar collaborators, network clustering and network assortativity by number of collaborators.

Because networks can be used to represent many complex systems, they have attracted considerable attention in physics, computer science, sociology, and many other disciplines. One of the most important areas of network science is the algorithmic detection of cohesive groups (i.e., "communities") of nodes. In this paper, we algorithmically detect communities in social networks and image data by optimizing multislice modularity. A key advantage of modularity optimization is that it does not require prior knowledge of the number or sizes of communities, and it is capable of finding network partitions that are composed of communities of different sizes. By optimizing multislice modularity and subsequently calculating diagnostics on the resulting network partitions, it is thereby possible to obtain information about network structure across multiple system scales. We illustrate this method on data from both social networks and images, and we find that optimization of multislice modularity performs well on these two tasks without the need for extensive problem-specific adaptation. However, improving the computational speed of this method remains a challenging open problem.; Comment: 3 pages, 2 figures, to appear in IEEE International Conference on Data Mining PhD forum conference proceedings

Collaboration networks arise when we map the connections between scientists which are formed through joint publications. These networks thus display the social structure of academia, and also allow conclusions about the structure of scientific knowledge. Using the computer science publication database DBLP, we compile relations between authors and publications as graphs and proceed with examining and quantifying collaborative relations with graph-based methods. We review standard properties of the network and rank authors and publications by centrality. Additionally, we detect communities with modularity-based clustering and compare the resulting clusters to a ground-truth based on conferences and thus topical similarity. In a second part, we are the first to combine DBLP network data with data from the Dagstuhl Seminars: We investigate whether seminars of this kind, as social and academic events designed to connect researchers, leave a visible track in the structure of the collaboration network. Our results suggest that such single events are not influential enough to change the network structure significantly. However, the network structure seems to influence a participant's decision to accept or decline an invitation.; Comment: ASONAM 2012: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Many collective human activities have been shown to exhibit universal patterns. However, the possibility of universal patterns across timing events of researcher migration has barely been explored at global scale. Here, we show that timing events of migration within different countries exhibit remarkable similarities. Specifically, we look at the distribution governing the data of researcher migration inferred from the web. Compiling the data in itself represents a significant advance in the field of quantitative analysis of migration patterns. Official and commercial records are often access restricted, incompatible between countries, and especially not registered across researchers. Instead, we introduce GeoDBLP where we propagate geographical seed locations retrieved from the web across the DBLP database of 1,080,958 authors and 1,894,758 papers. But perhaps more important is that we are able to find statistical patterns and create models that explain the migration of researchers. For instance, we show that the science job market can be treated as a Poisson process with individual propensities to migrate following a log-normal distribution over the researcher's career stage. That is, although jobs enter the market constantly, researchers are generally not "memoryless" but have to care greatly about their next move. The propensity to make k>1 migrations...

We describe a practical approach for visual exploration of research papers. Specifically, we use the titles of papers from the DBLP database to create what we call maps of computer science (MoCS). Words and phrases from the paper titles are the cities in the map, and countries are created based on word and phrase similarity, calculated using co-occurrence. With the help of heatmaps, we can visualize the profile of a particular conference or journal over the base map. Similarly, heatmap profiles can be made of individual researchers or groups such as a department. The visualization system also makes it possible to change the data used to generate the base map. For example, a specific journal or conference can be used to generate the base map and then the heatmap overlays can be used to show the evolution of research topics in the field over the years. As before, individual researchers or research groups profiles can be visualized using heatmap overlays but this time over the journal or conference base map. Finally, research papers or abstracts easily generate visual abstracts giving a visual representation of the distribution of topics in the paper. We outline a modular and extensible system for term extraction using natural language processing techniques...

The birth and decline of disciplines are critical to science and society. However, no quantitative model to date allows us to validate competing theories of whether the emergence of scientific disciplines drives or follows the formation of social communities of scholars. Here we propose an agent-based model based on a \emph{social dynamics of science,} in which the evolution of disciplines is guided mainly by the social interactions among scientists. We find that such a social theory can account for a number of stylized facts about the relationships between disciplines, authors, and publications. These results provide strong quantitative support for the key role of social interactions in shaping the dynamics of science. A "science of science" must gauge the role of exogenous events, such as scientific discoveries and technological advances, against this purely social baseline.

Co-words have been considered as carriers of meaning across different domains in studies of science, technology, and society. Words and co-words, however, obtain meaning in sentences, and sentences obtain meaning in their contexts of use. At the science/society interface, words can be expected to have different meanings: the codes of communication that provide meaning to words differ on the varying sides of the interface. Furthermore, meanings and interfaces may change over time. Given this structuring of meaning across interfaces and over time, we distinguish between metaphors and diaphors as reflexive mechanisms that facilitate the translation between contexts. Our empirical focus is on three recent scientific controversies: Monarch butterflies, Frankenfoods, and stem-cell therapies. This study explores new avenues that relate the study of co-word analysis in context with the sociological quest for the analysis and processing of meaning.

From its inception, a large part of the motivation for Cognitive Science has been the need for an interdisciplinary journal for the study of minds and intelligent systems. One threat to the interdisciplinarity of Cognitive Science, both the field and journal, is that it may become, or already be, too dominated by psychologists. In 2005, psychology was a keyword for 51% of submissions, followed distantly by linguistics (17%), artificial intelligence (13%), neuroscience (10%), computer science (9%), and philosophy (8%). The Institute for Scientific Information (ISI) gathers data not only on how individual articles cite one another, but also on macroscopic citation patterns among journals. Journals or sets of journals can be considered as proxies for fields. As fields become established, they often create journals. By studying the patterns of citations among journals that cite and are cited by Cognitive Science, we can better: 1) appreciate the scholarly ecology surrounding the journal and the journals role within this ecology, 2) establish competitor and alternate journals, and 3) determine the natural clustering of fields related to cognitive science.

The structural properties of the network generated by the editorial activities of the members of the boards of "Information Science & Library Science" journals are explored through network analysis techniques. The crossed presence of scholars on editorial boards, the phenomenon called interlocking editorship, is considered a proxy of the similarity of editorial policies. The evidences support the idea that this group of journals is better described as a set of only relatively connected subfields. In particular two main subfield are identified, consisting of research oriented journals devoted respectively to LIS and MIS. The links between these two subsets are weak. Around these two subsets there are a lot of (relatively) isolated professional journals or journals characterized more by their subject-matter content than by their focus on information flows. It is possible to suggest that this configuration of the network may be the consequence of the youthfulness of Information Science & Library Science, which has not permitted yet to reach a general consensus through scholars on research aims, methods and instruments.; Comment: 12 pages, 3 figures, 3 tables Accepted for publication. Journal of Informetrics