Dissertations / Theses: 'Human behavior'

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Roberts, Michael E. "Human collective behavior." [Bloomington, Ind.] : Indiana Unversity, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3330786.

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Thesis (Ph.D.)--Indiana University, Depts. of Psychological and Brain Sciences and Cognitive Science, 2008.
Title from PDF t.p. (viewed on Jul 22, 2009). Source: Dissertation Abstracts International, Volume: 69-10, Section: B, page: 6448. Advisers: Robert L. Goldstone; Peter M. Todd.

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Koutentakis, Dimitrios. "Modeling human driving behavior." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129895.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 81-84).
The goal of this thesis paper is to explore models that can predict and anticipate driver behaviors on the road and give probabilities on future actions of neighboring vehicles, while being lightweight enough to be formally verifiable. This thesis starts with looking into related work and doing a short literature review on previous work on driver models. We then talk about the available datasets used to perform such work, different models used (from classic regressions to neural networks) and finally present my approach and my results.
by Dimitrios Koutentakis.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science

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Kalwar, Santosh. "Human behavior on the Internet." Thesis, Lappeenranata University of Technology, 2009. http://www.kalwar.com.np.

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In this thesis, "Human behavior on the Internet", the human anxiety is conceptualized. The following questions have guided the writing of the thesis: How humans behave with the Internet technology? What goes in their mind? What kinds of behaviors are shown while using the Internet? What is the role of the content on the Internet and especially what are the types of anxiety behavior on the Internet? By conceptualization this thesis aims to provide a model for studying whether humans show signs of less or exacerbated anxiety while using the Internet.The empirical part of this thesis was built on new developed model and user study that utilizes that model. For the user study, the target users were divided into two groups based on their skill level. The user study used both qualitative and quantitative research methods. The qualitative research was conducted using interviews and observational analysis. The quantitative research was conducted in three iterations by using questionnaires and surveys.These results suggest that the significance of human on using technology would be integral part of such a study. The study also suggests that Internet has lulled humans with the sense of dependency to greater extent. In particular, the results identified seven main areas of human anxiety. These forms of anxiety require further studies to encompass human anxiety in more detail.

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Poletti, Piero. "Human Behavior in Epidemic Modelling." Doctoral thesis, Università degli studi di Trento, 2010. https://hdl.handle.net/11572/367834.

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Mathematical models represent a powerful tool for investigating the dynamics of human infection diseases, providing useful predictions about the spread of a disease and the effectiveness of possible control measures. One of the central aspects to understand the dynamics of human infection is the heterogeneity in behavioral patters adopted by the host population. Beyond control measures imposed by public authorities, human behavioral changes can be triggered by uncoordinated responses driven by the diffusion of fear in the general population or by the risk perception. In order to assess how and when behavioral changes can affect the spread of an epidemic, spontaneous social distancing - e.g. produced by avoiding crowded environments, using face masks or limiting travels - is investigated. Moreover, in order to assess whether vaccine preventable diseases can be eliminated through not compulsory vaccination programs, vaccination choices are investigated as well. The proposed models are based on an evolutionary game theory framework. Considering dynamical games allows explicitly modeling the coupled dynamics of disease transmission and human behavioral changes. Specifically, the information diffusion is modeled through an imitation process in which the convenience of different behaviors depends on the perceived risk of infection and vaccine side effects. The proposed models allow the investigation of the effects of misperception of risks induced by partial, delayed or incorrect information (either concerning the state of the epidemic or vaccine side effects) as well. The performed investigation highlights that a small reduction in the number of potentially infectious contacts in response to an epidemic and an initial misperception of the risk of infection can remarkably affect the spread of infection. On the other hand, the analysis of vaccination choices showed that concerns about proclaimed risks of vaccine side effects can result in widespread refusal of vaccination which in turn leads to drops in vaccine uptake and suboptimal vaccination coverage.

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Poletti, Piero. "Human Behavior in Epidemic Modelling." Doctoral thesis, University of Trento, 2010. http://eprints-phd.biblio.unitn.it/422/1/tesi.pdf.

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Mathematical models represent a powerful tool for investigating the dynamics of human infection diseases, providing useful predictions about the spread of a disease and the effectiveness of possible control measures. One of the central aspects to understand the dynamics of human infection is the heterogeneity in behavioral patters adopted by the host population. Beyond control measures imposed by public authorities, human behavioral changes can be triggered by uncoordinated responses driven by the diffusion of fear in the general population or by the risk perception. In order to assess how and when behavioral changes can affect the spread of an epidemic, spontaneous social distancing - e.g. produced by avoiding crowded environments, using face masks or limiting travels - is investigated. Moreover, in order to assess whether vaccine preventable diseases can be eliminated through not compulsory vaccination programs, vaccination choices are investigated as well. The proposed models are based on an evolutionary game theory framework. Considering dynamical games allows explicitly modeling the coupled dynamics of disease transmission and human behavioral changes. Specifically, the information diffusion is modeled through an imitation process in which the convenience of different behaviors depends on the perceived risk of infection and vaccine side effects. The proposed models allow the investigation of the effects of misperception of risks induced by partial, delayed or incorrect information (either concerning the state of the epidemic or vaccine side effects) as well. The performed investigation highlights that a small reduction in the number of potentially infectious contacts in response to an epidemic and an initial misperception of the risk of infection can remarkably affect the spread of infection. On the other hand, the analysis of vaccination choices showed that concerns about proclaimed risks of vaccine side effects can result in widespread refusal of vaccination which in turn leads to drops in vaccine uptake and suboptimal vaccination coverage.

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Wang, Wei. "Human Face and Behavior Analysis." Doctoral thesis, Università degli studi di Trento, 2018. https://hdl.handle.net/11572/367945.

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Human face and behavior analysis are very important research topics in the field of computer vision and they have broad applications in our everyday life. For instance, face alignment, face aging, face expression analysis and action recognition have been well studied and applied for security and entertainment. With these face analyzing techniques (e.g., face aging), we could enhance the performance of cross-age face verification system which now has been used for banks and electronic devices to recognize their clients. With the help of action recognition system, we could better summarize the user uploaded videos or generate logs for surveillance videos. This could help us retrieve the videos more accurately and easily. The dictionary learning and neural networks are powerful machine learning models for these research tasks. Initially, we focus on the multi-view action recognition task. First, a class-wise dictionary is pre-trained which encourages the sparse representations of the between-class videos from different views to lie close by. Next, we integrate the classifiers and the dictionary learning model into a unified model to learn the dictionary and classifiers jointly. For face alignment, we frame the standard cascaded face alignment problem as a recurrent process by using a recurrent neural network. Importantly, by combining a convolutional neural network with a recurrent one we alleviate hand-crafted features to learn task-specific features. For human face aging task, it takes as input a single image and automatically outputs a series of aged faces. Since human face aging is a smooth progression, it is more appropriate to age the face by going through smooth transitional states. In this way, the intermediate aged faces between the age groups can be generated. Towards this target, we employ a recurrent neural network. The hidden units in the RFA are connected autoregressively allowing the framework to age the person by referring to the previous aged faces. For smile video generation, one person may smile in different ways (e.g., closing/opening the eyes or mouth). This is a one-to-many image-to-video generation problem, and we introduce a deep neural architecture named conditional multi-mode network (CMM-Net) to approach it. A multi-mode recurrent generator is trained to induce diversity and generate K different sequences of video frames.

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Wang, Wei. "Human Face and Behavior Analysis." Doctoral thesis, University of Trento, 2018. http://eprints-phd.biblio.unitn.it/2925/1/phd_thesis.pdf.

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Human face and behavior analysis are very important research topics in the field of computer vision and they have broad applications in our everyday life. For instance, face alignment, face aging, face expression analysis and action recognition have been well studied and applied for security and entertainment. With these face analyzing techniques (e.g., face aging), we could enhance the performance of cross-age face verification system which now has been used for banks and electronic devices to recognize their clients. With the help of action recognition system, we could better summarize the user uploaded videos or generate logs for surveillance videos. This could help us retrieve the videos more accurately and easily. The dictionary learning and neural networks are powerful machine learning models for these research tasks. Initially, we focus on the multi-view action recognition task. First, a class-wise dictionary is pre-trained which encourages the sparse representations of the between-class videos from different views to lie close by. Next, we integrate the classifiers and the dictionary learning model into a unified model to learn the dictionary and classifiers jointly. For face alignment, we frame the standard cascaded face alignment problem as a recurrent process by using a recurrent neural network. Importantly, by combining a convolutional neural network with a recurrent one we alleviate hand-crafted features to learn task-specific features. For human face aging task, it takes as input a single image and automatically outputs a series of aged faces. Since human face aging is a smooth progression, it is more appropriate to age the face by going through smooth transitional states. In this way, the intermediate aged faces between the age groups can be generated. Towards this target, we employ a recurrent neural network. The hidden units in the RFA are connected autoregressively allowing the framework to age the person by referring to the previous aged faces. For smile video generation, one person may smile in different ways (e.g., closing/opening the eyes or mouth). This is a one-to-many image-to-video generation problem, and we introduce a deep neural architecture named conditional multi-mode network (CMM-Net) to approach it. A multi-mode recurrent generator is trained to induce diversity and generate K different sequences of video frames.

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Ellis, Jon E. Martin Michael W. "Human behavior representation of military teamwork." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Jun%5FEllis.pdf.

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Thesis (M.S. in Modeling, Virtual Environment and Simulation (MOVES))--Naval Postgraduate School, June 2006.
Thesis Advisor(s): Christian Darken and Jeffrey Crowson. "June 2006." Includes bibliographical references (p. 73-75). Also available in print.

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Wright, James Robert. "Modeling human behavior in strategic settings." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58840.

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Increasingly, electronic interactions between individuals are mediated by specialized algorithms. One might hope to optimize the relevant algorithms for various objectives. An aspect of online platforms that complicates such optimization is that the interactions are often strategic: many agents are involved, all with their own distinct goals and priorities, and the outcomes for each agent depend both on their own actions, and upon the actions of the other agents. My thesis is that human behavior can be predicted effectively in a wide range of strategic settings by a single model that synthesizes known deviations from economic rationality. In particular, I claim that such a model can predict human behavior better than the standard economic models. Economic mechanisms are currently designed under behavioral assumptions (i.e., full rationality) that are known to be unrealistic. A mechanism designed based on a more accurate model of behavior will be more able to achieve its goal. In the first part of the dissertation, we develop increasingly sophisticated data-driven models to predict human behavior in strategic settings. We begin by applying machine learning techniques to compare many existing models from behavioral game theory on a large body of experimental data. We then construct a new family of models called quantal cognitive hierarchy (QCH), which have even better predictive performance than the best of the existing models. We extend this model with a richer notion of nonstrategic behavior that takes into account features such as fairness, optimism, and pessimism, yielding further performance improvements. Finally, we perform some initial explorations into applying techniques from deep learning in order to automatically learn features of strategic settings that influence human behavior. A major motivation for modeling human strategic behavior is to improve the design of practical mechanisms for real-life settings. In the second part of the dissertation, we study an applied strategic setting (peer grading), beginning with an analysis of the question of how to optimally apply teaching assistant resources to incentivize students to grade each others' work accurately. We then report empirical results from using a variant of this system in a real-life undergraduate class.
Science, Faculty of
Computer Science, Department of
Graduate

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Khosla, Aditya. "Predicting human behavior using visual media." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/109001.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 161-173).
The ability to predict human behavior has applications in many domains ranging from advertising to education to medicine. In this thesis, I focus on the use of visual media such as images and videos to predict human behavior. Can we predict what images people remember or forget? Can we predict the type of images people will like? Can we use a photograph of someone to determine their state of mind? These are some of the questions I tackle in this thesis. Through my work, I demonstrate: (1) It is possible to predict with near human-level correlation, the probability with which people will remember images, (2) it is possible to predictably modify the extent to which a face photograph is remembered, (3) it is possible to predict, with a high correlation, the number of views an image will receive even before it is uploaded, (4) it is possible to accurately identify the gaze of people in images, both from the perspective of a device, and third-person. Further, I develop techniques to visualize and understand machine learning algorithms that could help humans better understand themselves through the analysis of algorithms capable of predicting behavior. Overall, I demonstrate that visual media is a rich resource for the prediction of human behavior.
by Aditya Khosla.
Ph. D.

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11

Mahamadi, Abdelrhman. "Bond Graph Models for Human Behavior." University of Akron / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=akron1479485016074583.

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Martin, Michael W. "Human behavior representation of military teamwork." Thesis, Monterey, California. Naval Postgraduate School, 2006. http://hdl.handle.net/10945/2754.

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This work presents a conceptual structure for the behaviors of artificial intelligence agents, with emphasis on creating teamwork through individual behaviors. The goal is to set up a framework which enables teams of simulation agents to behave more realistically. Better team behavior can lend a higher fidelity of human behavior representation in a simulation, as well as provide opportunities to experiment with the factors that create teamwork. The framework divides agent behaviors into three categories: leadership, individual, and team-enabling. Leadership behaviors consist of planning, decision-making, and delegating. Individual behaviors consist of moving, shooting, environment-monitoring, and self-monitoring. Team-enabling behaviors consist of communicating, synchronizing actions, and team member monitoring. These team-enabling behaviors augment the leadership and individual behaviors at all phases of an agent's thought process, and create aggregate team behavior that is a hybrid of emergent and hierarchical teamwork. The net effect creates, for each agent, options and courses of action which are sub-optimal from the individual agent's standpoint, but which leverage the power of the team to accomplish objectives. The individual behaviors synergistically combine to create teamwork, allowing a group of agents to act in such a manner that their overall effectiveness is greater than the sum of their individual contributions.
US Army (USA) author.

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Przemeck, Judith. "Personnel economics, incentives and human behavior /." [Bonn] : [s.n.], 2009. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=017381917&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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James, Hannah Victoria Arnison. "Becoming human : the emergence of modern human behaviour within South Asia." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609772.

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Morken, Gunnar. "Seasonal Variation of Human Mood and Behavior." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Medicine, 2001. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-326.

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Seasonal variations of mood, behavior and physiology have been of increasing interest. At least two different seasonal rhythms seem to exist: Descriptions of Seasonal Affective Disorder (SAD) with increased weight, increased sleep and fatigue during winter have attracted attention in academic psychiatry and in the general public the last two decades. In addition to such a difference in mood, weight and sleep between summer and winter, many studies describe a spring and fall increase in frequency of suicides and of admissions to hospital for mood disorders. In searching for a possible etiology for these seasonal changes, the main focus has been on variations in length of day.

The objective of this thesis was to study the existence and pattern of seasonal variation in some forms of behavior and of psychiatric illness among children and adults in Norway. Possible statistical connections between seasonal variations of behavior and changes in length of day and the influences of latitude, sex and age were also studied.

The monthly numbers of incidents in different groups were studied: All suicides in Norway 1969-96 (N=14.503), admissions to hospital for depression and mania in some hospitals 1992-96 (N=4.341), all violent episodes recorded by the police in Norway 1991-97 (N=82.537), all patient-staff incidents in a psychiatric department 1990-97 (N=502), all telephone calls to the Red Cross help-line for children and adolescents in Norway 1996-98 (N=691.787calls, 220.602 conversations) and in Trondheim, Norway 1991-97 (N=80.983 calls, 22.698 conversations) were included in the thesis. The monthly frequencies of these incidents were compared to an expected equal daily frequency of incidents through the year. Changes with increasing age and increasing latitude were examined. Correlations between the monthly frequencies of incidents and the length of day, with maximum impact at midsummer, and correlations between the monthly frequencies of incidents and the speed of change in length of day, with maximum impact at the equinoxes, were also studied.

In this thesis, an increased activity in April-June and in October-November is described for all the groups that were studied. In summer and winter there is less activity than in the rest of the year. Among children calling the help-line, a steady diminishing seasonal variation in number of calls with increasing age from 7 to 17 years of age and an increasing seasonal variation in number of calls with increasing latitude were found. Also the seasonal variation of violence increases with increasing latitude in Norway. Among men there is a correlation between the monthly number of suicides and the monthly number of admissions for mania and a correlation between the monthly number of suicides and the monthly number of admissions for depression. Among women there is a diminishing seasonal variation of admissions for depressions with increasing age. The monthly frequency of violence in Norway and the monthly frequency of calls to the Red Cross help-Line for children and adolescents correlated with a delay of 1-2 months with the monthly change in length of day with maximum impact at the equinoxes.

The results in the thesis correspond with earlier studies describing an increase in the frequency of suicides and an increase in admissions for depressions in spring and fall. A corresponding rhythm for other forms of human behavior is described in the present thesis, indicating that the seasonal rhythm of psychiatric illness reflects a seasonal rhythm of behavior in greater parts of the population. The seasonal variation in behavior seems to increase with increasing latitude, to be more dramatic in the northern than in the southern parts of Norway. In this thesis results supporting a hypothesis of human behavior being influenced by changes in length of day are given. Changes in length of day may induce changes in sleep and other disturbances in the daily rhythm that could change mood and other emotional qualities in vulnerable individuals. The demands on our capability to adapt to changes in length of day are largest at the equinoxes.

Årstidsvariasjon av sinnstemning og adferd.

Det er økende interesse for årstidsvariasjon av adferd og av forekomsten av psykiske lidelser. Det synes å foreligge minst to ulike årstidsrytmer i befolkningen; Størst oppmerksomhet har oppdagelsen av vinterdepresjon karakterisert ved tristhet, tretthet, økt vekt og forlenget søvn vakt. I tillegg til en slik forskjell i humør, vekt og søvn mellom sommer og vinter, er det en rekke beskrivelser av overhyppighet av selvmord og av innleggelser i sykehus for depresjoner vår og høst. Årsakene til disse to ulike årstidsrytmene er ikke kjent, men man har antatt at variasjon i dagslengde gjennom året spiller en rolle.

Hensikten med denne avhandlingen har vært å undersøke om det er årstidsvariasjon i forekomsten av ulike former for adferd og av psykiske lidelser hos barn og voksne i Norge. Videre er eventuelle statistiske sammenhenger mellom adferd og dagslengde gjennom året undersøkt. Til sist er forskjeller i årstidsrytme knyttet til breddegrad, alder og kjønn undersøkt.

Antallet hendelser pr måned i ulike grupper ble studert; Alle selvmord i Norge 1969-96 (N=14.503), innleggelser for depresjon og mani i en del sykehus 1992-96 (N=4.341), alle registrerte voldsepisoder i Norge 1991-97 (N= 82.537), personalskader i et psykiatrisk sykehus 1991-97 (N=502), alle telefoner til Røde Kors Kontakttelefon for barn og unge i Norge 1996–98 (N=691.787 oppringninger, 220.602 samtaler) og i Trondheim 1991-97 (N=80.983 oppringninger, 22.698 samtaler) ble inkludert i arbeidet. Hyppigheten av alle disse hendelsene i hver måned ble sammenlignet med en forventet lik fordeling av hendelsene året igjennom. Endringer med økende alder og med økende breddegrad ble undersøkt. Videre ble det gjort sammenligninger med dagslengde som er lengst ved sommersolverv og kortest ved vintersolverv, og sammenligninger med endringer av dagslengde som er hurtig ved vår og høstjamndøgn og sakte ved solvervene.

I alle disse materialene er det en økt aktivitet april – juni og oktober – november, videre er det stille perioder om vinteren og om sommeren. Blant barn som ringer kontakttelefonen er det gradvis avtagende årstidsvariasjon av henvendelser med økende alder fra 7 til 17 år og økende årstidsvariasjon i antallet henvendelser jo lenger nord man kommer i Norge. Også årstidsvariasjonen av vold i Norge endrer seg jo lengre nord man kommer i landet.

Blant menn er der en korrelasjon mellom det månedlige antallet av selvmord og av innleggelser for mani og mellom antallet selvmord og innleggelser for depresjon. Blant kvinner er det en avtagende årstidsvariasjon av innleggelser for depresjon med økende alder.

Den månedlige endring av dagslengde som er raskest ved jamndøgnene korrelerer med en viss forsinkelse med forekomsten av vold i Norge og med antallet oppringninger til Barn og Unges kontakttelefon.

Funnene i avhandlingen er i samsvar med tidligere beskrivelser av en markert økning av suicid og av innleggelser for depresjoner om våren og til dels om høsten. I avhandlingen er en tilsvarende rytme funnet for annen adferd. Dette tyder på at årstidsrytmen av psykiatrisk sykelighet avspeiler en årstidsrytme av adferd i store deler av befolkningen. Videre ser det ut til at forskjellene i adferd gjennom året blir mer markerte jo lengre nord man kommer i landet. I avhandlingen er det funn som støtter en hypotese om at endringer i dagslengde påvirker mennesket, det er mulig at dette skjer gjennom endret søvn og andre forstyrrelser i døgnrytmen. Vår døgnrytme er utsatt for størst krav til å tilpasse seg hurtige endringer i lysforhold rundt jamndøgnene.

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Engmo, Vidar. "Representation of Human Behavior in Military Simulations." Thesis, Norwegian University of Science and Technology, Department of Telematics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9798.

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The purpose of this master thesis was to investigate the psychological and computational basis for human behavior representation (HBR) in military simulations and identify problem areas of existent software agent frameworks that provide computer generated forces (CGF) with human like cognitive abilities. The master thesis identifies psychological properties that influence human cognition in an operational environment through a theoretical study of operational and cognitive psychology. The psychological properties of human cognition are then connected to artificial intelligence through a theoretical study of agents and multi-agent systems and form the foundation for identifying general HBR properties. The HBR properties are used as evaluation markers that constitute the basis for constructing an evaluation of relevant agent frameworks thereby visualizing their strengths and weaknesses. The problem areas of incorporating artificial intelligence into CGF are further concretized by the development of a demonstrator that interacts with a synthetic environment. The demonstrator is an implementation of a tank platoon in the agent framework Jadex. The synthetic environment is provided by VR-Forces which is a product by MÄK technologies. The thesis makes a distinction between the conceptual structure of agent frameworks and their actual implementation. According to this master thesis it is the output of the agent framework that is the most important feature not how the output came into being. Producing the correct output requires the selection of the correct tools for the job. The selection of an agent framework should be taken on the background of an evaluation of the simulation requirements. A large portion of the development time is consumed by the development of application and communication interfaces. The problem is a result of lacking standardization and that most cognitive agent frameworks are experimental in nature. In addition the artificial intelligence (AI) in such simulations is often dived into levels, where the synthetic environment takes care of low-level AI and the agent framework the high-level AI. Tight synchronization between low and high-level AI is important if one wishes to create sensible behavior. The purpose of an agent framework in conjunction with CGF is thereby ensuring rapid development and testing of behavior models.

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Hartford, Jason Siyanda. "Deep learning for predicting human strategic behavior." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59559.

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Predicting the behavior of human participants in strategic settings is an important problem for applications that rely on game theoretic reasoning to design mechanisms or allocate resources. Most existing work either assumes that participants are perfectly rational, or attempts to directly model each participant's cognitive processes based on insights from cognitive psychology and experimental economics. In this work, we present an alternative, a deep learning-based approach that automatically performs cognitive modeling without relying on such expert knowledge. We introduce a novel architecture that allows a single network to generalize across normal form games with varying numbers of actions. We show that the architecture generalists the most successful existing models and that its performance significantly improves upon that of the previous state of the art, which relies on expert-constructed features.
Science, Faculty of
Computer Science, Department of
Graduate

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Edlund, Mattias. "Artificial Intelligence in Games : Faking Human Behavior." Thesis, Uppsala universitet, Institutionen för speldesign, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-258222.

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This paper examines the possibilities of faking human behavior with artificial intelligence in computer games, by using efficient methods that save valuable development time and also creates a more rich experience for the players of a game. The specific implementation of artificial intelligence created and discussed is a neural network controlling a finite-state machine. The objective was to mimic human behavior rather than simulating true intelligence. A 2D shooter game is developed and used for experiments performed with human and artificial intelligence controlled players. The game sessions played were recorded in order for other humans to replay. Both players and spectators of the game sessions left feedbacks and reports that could later be analyzed. The data collected from these experiments was then analyzed, and reflections were made on the entire project. Tips and ideas are proposed to developers of shooter games who are interested in making human-like artificial intelligence. Conclusions are made and extra information is provided in order to further iterate on this research.
Denna rapport undersöker möjligheterna att förfalska mänskligt beteende genom artificiell intelligens i datorspel, med hjälp av effektiva metoder som sparar värdefull utvecklingstid och som även skapar en rikare upplevelse för spelare. Den specifika implementationen av artificiell intelligens som utvecklas och diskuteras är ett neuralt nätverk som kontrollerar en finite-state machine. Målet var att efterlikna mänskligt beteende snarare än att simulera verklig intelligens. Ett 2D shooter-spel utvecklas och används för utförda experiment med mänskliga och artificiell intelligens-kontrollerade spelare. De sessioner som spelades under experimenten spelades in, för att sedan låta andra människor titta på inspelningarna. Både spelare och åskådare av spelsessionerna lämnade återkoppling och rapporter för senare analysering. Datan som samlats in från experimenten analyserades, och reflektioner utfördes på hela projektet. Tips och idéer presenteras till utvecklare av shooter-spel som är intresserade av en mer människolik artificiell intelligens. Slutsatser läggs fram och extra information presenteras för att kunna fortsätta iterera vidare på denna undersökning.

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QUEVA, CAROLINE. "Human following behavior for an autonomousmobile robot." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-142474.

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Robots are used more and more to change people's life. In recent times, some robots have found their place in houses. Those robots are specic ones as for example robotic vacuum cleaners. The next step in robotics' evolution is to make people live with robots in everyday life. This project attempts to make a robot follow a person. To achieve that, a people detector using a RGB-D sensor is implemented. The result of this detector is sent to a tracking algorithm using depth segmentation and a region growing algorithm. At the end, a navigation algorithm containing path planning and obstacle avoidance is used to make the mobile robot move. This method can make the robot follow a person that walks slowly in front of it. Suggestions are presented to improve the method and adapt it to everyday life. Thanks to this study, mobile robots can be used in personal home and assist people. An application could be for example to follow a person that needs medical assistance and help this person when necessary.
Robotar används mer och mer för att förbättra människors liv. På senare tid har robotarna anlänt i vara hus. Dessa robotar är specialiserade, till exempel är de robotiserade dammsugare. I denna robotikevolution, är nästa steg att få människor att leva varje dag med robotar. Det här projektet försöker få en robot att följa en person. För att uppnå målet, har en detektor för människor implementerats, baserat på en RGB-D sensor. Denna detektors resultat sänds till en följningsalgoritm som använder segmentering baserat på djupinformation och region growing. Till slut, används ett navigeringssystem som innehåller vägplanering och hinderundvikande för att få den mobila roboten att röra sig säkert i omgivningen. Den här metoden får roboten att följa en person som går sakta framför roboten. Flera förslag till förbättringar föreslås för att hantera alla de komplikationer som vardagliga miljöer erbjuder. Tack vare det här projektet, kan mobila robotar användas i husen och hjälpa personer. En tillämpning är till exempel att följa en person som behöver medicinsk bistånd och hjälpa den här personen när det är nödvändigt.

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Haller, Karon Anne. "The double bind : paradox in human behavior /." Access Digital Full Text version, 1989. http://pocketknowledge.tc.columbia.edu/home.php/bybib/1085793x.

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Shah, Fahad. "Modeling human group behavior in virtual worlds." Doctoral diss., University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4704.

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Virtual worlds and massively-multiplayer online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. They provide new outlets for human social interaction that differ from both face-to-face interactions and non-physically-embodied social networking tools such as Facebook and Twitter. We aim to study group dynamics in these virtual worlds by collecting and analyzing public conversational patterns of users grouped in close physical proximity. To do this, we created a set of tools for monitoring, partitioning, and analyzing unstructured conversations between changing groups of participants in Second Life, a massively multi-player online user-constructed environment that allows users to construct and inhabit their own 3D world. Although there are some cues in the dialog, determining social interactions from unstructured chat data alone is a difficult problem, since these environments lack many of the cues that facilitate natural language processing in other conversational settings and different types of social media. Public chat data often features players who speak simultaneously, use jargon and emoticons, and only erratically adhere to conversational norms. Humans are adept social animals capable of identifying friendship groups from a combination of linguistic cues and social network patterns. But what is more important, the content of what people say or their history of social interactions? Moreover, is it possible to identify whether people are part of a group with changing membership merely from general network properties, such as measures of centrality and latent communities? These are the questions that we aim to answer in this thesis. The contributions of this thesis include: 1) a link prediction algorithm for identifying friendship relationships from unstructured chat data 2) a method for identifying social groups based on the results of community detection and topic analysis. The output of these two algorithms (links and group membership) are useful for studying a variety of research questions about human behavior in virtual worlds. To demonstrate this we have performed a longitudinal analysis of human groups in different regions of the Second Life virtual world. We believe that studies performed with our tools in virtual worlds will be a useful stepping stone toward creating a rich computational model of human group dynamics.
ID: 030646202; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (Ph.D.)--University of Central Florida, 2011.; Includes bibliographical references (p. 162-183).
Ph.D.
Doctorate
Computer Science
Engineering and Computer Science
Computer Science

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Ayers, Douglas. "Monitoring human behavior in an office environment." Honors in the Major Thesis, University of Central Florida, 1998. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/21.

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This item is only available in print in the UCF Libraries. If this is your Honors Thesis, you can help us make it available online for use by researchers around the world by following the instructions on the distribution consent form at http://library.ucf.edu/Systems/DigitalInitiatives/DigitalCollections/InternetDistributionConsentAgreementForm.pdf You may also contact the project coordinator, Kerri Bottorff, at kerri.bottorff@ucf.edu for more information.
Bachelors
Arts and Sciences
Computer Science

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Kacem, Anis. "Novel geometric tools for human behavior understanding." Thesis, Lille 1, 2018. http://www.theses.fr/2018LIL1I076/document.

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Récemment, le développement de systèmes intelligents dédiés pour la compréhension du comportement humain est devenu un axe de recherche très important. En effet, il est très important de comprendre le comportement humain pour rendre les machines capables d'aider et interagir avec les humains. Pour cela, plusieurs approches de l'état de l'art commencent par détecter automatiquement un ensemble de points 2D ou 3D, appelés marqueurs, sur le corps et/ou le visage humain à partir de données visuelles. L’analyse des séquences temporelles de ces marqueurs pose plusieurs défis dus aux erreurs de suivi et aux variabilités temporelles et de pose. Dans cette thèse, nous proposons deux nouvelles représentations spatio-temporelles avec des outils de calcul appropriés pour la compréhension du comportement humain. La première consiste à représenter une séquence temporelle de marqueurs par une trajectoire de matrices de Gram. Les matrices de Gram sont des matrices semi-définies positives de rang fixe et vivent dans un espace non-linéaire dans lequel les outils d’apprentissage automatique conventionnels ne peuvent pas être appliqués directement. Nous évaluons l’efficacité de notre approche dans plusieurs applications, impliquant des marqueurs 2D et 3D de visages et de corps humain, tels que la reconnaissance des émotions à partir des expressions faciales la reconnaissance d’actions et des émotions à partir des données de profondeur 3D. La deuxième représentation proposée dans cette thèse est basée sur les coordonnées barycentriques des marqueurs de visages 2D. Cette représentation permet d’utiliser les outils de calcul et d’apprentissage automatique tels que les techniques d’apprentissage de métrique. Les résultats obtenus en reconnaissance des expressions faciales et en mesure automatique de la sévérité de la dépression à partir du visage montrent tout l’intérêt de la représentation barycentrique combinée à des techniques d’apprentissage automatique. Les résultats obtenus avec les deux méthodes proposées sur des bases de données réelles montrent la compétitivité de nos approches avec les méthodes récentes de l’état de l’art
Developing intelligent systems dedicated to human behavior understanding has been a very hot research topic in the few recent decades. Indeed, it is crucial to understand the human behavior in order to make machines able to interact with, assist, and help humans in their daily life.. Recent breakthroughs in computer vision and machine learning have made this possible. For instance, human-related computer vision problems can be approached by first detecting and tracking 2D or 3D landmark points from visual data. Two relevant examples of this are given by the facial landmarks detected on the human face and the skeletons tracked along videos of human bodies. These techniques generate temporal sequences of landmark configurations, which exhibit several distortions in their analysis, especially in uncontrolled environments, due to view variations, inaccurate detection and tracking, missing data, etc. In this thesis, we propose two novel space-time representations of human landmark sequences along with suitable computational tools for human behavior understanding. Firstly, we propose a representation based on trajectories of Gram matrices of human landmarks. Gram matrices are positive semi-definite matrices of fixed rank and lie on a nonlinear manifold where standard computational and machine learning techniques could not be applied in a straightforward way. To overcome this issue, we make use of some notions of the Riemannian geometry and derive suitable computational tools for analyzing Gram trajectories. We evaluate the proposed approach in several human related applications involving 2D and 3D landmarks of human faces and bodies such us emotion recognition from facial expression and body movements and also action recognition from skeletons. Secondly, we propose another representation based on the barycentric coordinates of 2D facial landmarks. While being related to the Gram trajectory representation and robust to view variations, the barycentric representation allows to directly work with standard computational tools. The evaluation of this second approach is conducted on two face analysis tasks namely, facial expression recognition and depression severity level assessment. The obtained results with the two proposed approaches on real benchmarks are competitive with respect to recent state-of-the-art methods

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Devanne, Maxime. "3D human behavior understanding by shape analysis of human motion and pose." Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10138/document.

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L'émergence de capteurs de profondeur capturant la structure 3D de la scène et du corps humain offre de nouvelles possibilités pour l'étude du mouvement et la compréhension des comportements humains. Cependant, la conception et le développement de modules de reconnaissance de comportements à la fois précis et efficaces est une tâche difficile en raison de la variabilité de la posture humaine, la complexité du mouvement et les interactions avec l'environnement. Dans cette thèse, nous nous concentrons d'abord sur le problème de la reconnaissance d'actions en représentant la trajectoire du corps humain au cours du temps, capturant ainsi simultanément la forme du corps et la dynamique du mouvement. Le problème de la reconnaissance d'actions est alors formulé comme le calcul de similitude entre la forme des trajectoires dans un cadre Riemannien. Les expériences menées sur quatre bases de données démontrent le potentiel de la solution en termes de précision/temps de latence de la reconnaissance d'actions. Deuxièmement, nous étendons l'étude aux comportements plus complexes en analysant l'évolution de la forme de la posture pour décomposer la séquence en unités de mouvement. Chaque unité de mouvement est alors caractérisée par la trajectoire de mouvement et l'apparence autour des mains, de manière à décrire le mouvement humain et l'interaction avec les objets. Enfin, la séquence de segments temporels est modélisée par un classifieur Bayésien naïf dynamique. Les expériences menées sur quatre bases de données évaluent le potentiel de l'approche dans différents contextes de reconnaissance et détection en ligne de comportements
The emergence of RGB-D sensors providing the 3D structure of both the scene and the human body offers new opportunities for studying human motion and understanding human behaviors. However, the design and development of models for behavior recognition that are both accurate and efficient is a challenging task due to the variability of the human pose, the complexity of human motion and possible interactions with the environment. In this thesis, we first focus on the action recognition problem by representing human action as the trajectory of 3D coordinates of human body joints over the time, thus capturing simultaneously the body shape and the dynamics of the motion. The action recognition problem is then formulated as the problem of computing the similarity between shape of trajectories in a Riemannian framework. Experiments carried out on four representative benchmarks demonstrate the potential of the proposed solution in terms of accuracy/latency for a low-latency action recognition. Second, we extend the study to more complex behaviors by analyzing the evolution of the human pose shape to decompose the motion stream into short motion units. Each motion unit is then characterized by the motion trajectory and depth appearance around hand joints, so as to describe the human motion and interaction with objects. Finally, the sequence of temporal segments is modeled through a Dynamic Naive Bayesian Classifier. Experiments on four representative datasets evaluate the potential of the proposed approach in different contexts, including recognition and online detection of behaviors

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Gutiérrez, Ezequiel Benito. "Other selves friendship as moral perfection in Aristotle's Nichomachean ethics /." Online full text .pdf document, available to Fuller patrons only, 2004. http://www.tren.com.

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Poskus, Virgilijus. "Contemporary egoistic and altruistic interpretations of Aristotle's doctrine of friendship." Online full text .pdf document, available to Fuller patrons only, 2004. http://www.tren.com.

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Hennings, Chad F. "Designing realistic human behavior into multi-agent systems." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2001. http://handle.dtic.mil/100.2/ADA397090.

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Thesis (M.S. in Modeling, Virtual Environments and Simulation (MOVES))--Naval Postgraduate School, Sept. 2001.
Thesis advisors: Hiles, John ; Darken, Rudolph. "September 2001." Includes bibliographical references (p. 59). Also Available in print.

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Johansson, Anders. "Context dependent adaptation of biting behavior in human." Doctoral thesis, Umeå universitet, Fysiologi, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-91664.

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The focus of this thesis was to study an action that humans perform regularly, namely, to hold a morsel between the teeth and split it into smaller pieces. Three different issues related to this biting behavior were addressed: (1) the effect of reduced periodontal tissues on food holding and splitting behavior; (2) the behavioral consequences of performing different bite tasks with different functional requirements, i.e., to split a peanut half resting on a piece of chocolate or to split both the peanut and the chocolate; and (3) the reflex modulations resulting from such a change in the intended bite action. The main conclusions from the experimental studies were the following: First, periodontitis, an inflammatory disease that destroys the periodontal ligaments and the embedded periodontal mechanoreceptors, causes significant impairments in the masticatory ability: the manipulative bite forces when holding a morsel are elevated compared to a matched control population and the bite force development prior to food split is altered. These changes are likely due to a combination of reduced sensory information from the damaged ligaments and to changes in the bite strategy secondary to the unstable oral situation. Second, people exploit the anatomy of jaw-closing muscles to regulate the amount of bite force that dissipates following a sudden unloading of the jaw. Such control is necessary because without mechanisms that quickly halt jaw-closing movements after sudden unloading, the impact forces when the teeth collide could otherwise damage both the teeth and related soft tissues. Splitting a piece of chocolate, for instance, regularly requires >100N of bite force and the jaws collide within 5 ms of a split. On the other hand, when biting through heterogeneous food, the bite force needs to be kept high until the whole morsel is split. The required regulation is achieved by differentially engaging parts of the masseter muscles along the anteroposterior axis of the jaw to exploit differences between muscle portions in their bite force generating capacity and muscle shortening velocity. Finally, the reflex evoked by suddenly unloading the jaw—apparent only after the initial bite force dissipation—is modulated according to the bite intention. That is, when the intention is to bite through food items with multiple layers, the reflex response in the jaw opening muscles following a split is small, thus minimizing the bite force reduction. In contrast, when the intention is to rapidly decrease the bite force once a split has occurred, the reflex response is high. This pattern of reflex modulation is functionally beneficial when biting through heterogeneous food in a smooth manner. The presented studies show the significance of integrating cognitive, physiological and anatomical aspects when attempting to understand human masticatory control.

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Moriarty, Christopher. "LEARNING HUMAN BEHAVIOR FROM OBSERVATION FOR GAMING APPLICATIONS." Master's thesis, University of Central Florida, 2007. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3354.

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The gaming industry has reached a point where improving graphics has only a small effect on how much a player will enjoy a game. One focus has turned to adding more humanlike characteristics into computer game agents. Machine learning techniques are being used scarcely in games, although they do offer powerful means for creating humanlike behaviors in agents. The first person shooter (FPS), Quake 2, is an open source game that offers a multi-agent environment to create game agents (bots) in. This work attempts to combine neural networks with a modeling paradigm known as context based reasoning (CxBR) to create a contextual game observation (CONGO) system that produces Quake 2 agents that behave as a human player trains them to act. A default level of intelligence is instilled into the bots through contextual scripts to prevent the bot from being trained to be completely useless. The results show that the humanness and entertainment value as compared to a traditional scripted bot have improved, although, CONGO bots usually ranked only slightly above a novice skill level. Overall, CONGO is a technique that offers the gaming community a mode of game play that has promising entertainment value.
M.S.Cp.E.
School of Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Engineering MSCpE

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Olguín, Olguín Daniel. "Sociometric badges : wearable technology for measuring human behavior." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/42169.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.
Includes bibliographical references (p. 137-144).
We present the design, implementation and deployment of a wearable computing research platform for measuring and analyzing human behavior in a variety of settings and applications. We propose the use of wearable sociometric badges capable of automatically measuring the amount of face-to-face interaction, conversational time, physical proximity to other people, and physical activity levels using social signals derived from vocal features, body motion, and relative location to capture individual and collective patterns of behavior. Our goal is to be able to understand how patterns of behavior shape individuals and organizations. We attempt to use on-body sensors in large groups of people for extended periods of time in naturalistic settings for the purpose of identifying, measuring, and quantifying social interactions, information flow, and organizational dynamics. We deployed this research platform in a group of 22 employees working in a real organization over a period of one month. Using these automatic measurements we were able to predict employees' self-assessment of productivity, job satisfaction, and their own perception of group interaction quality. An initial exploratory data analysis indicates that it is possible to automatically capture patterns of behavior using this wearable platform.
by Daniel Olguín Olguín.
S.M.

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Gips, Jonathan Peter. "Social motion : mobile networking through sensing human behavior." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/37384.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006.
"September 2006."
Includes bibliographical references (leaves 62-65).
Low-level sensors can provide surprisingly high-level information about social interactions. The goal of this thesis is to define the components of a framework for sensing social context with mobile devices. We describe several sensing technologies - including infrared transceivers, radio frequency scanners, and accelerometers - that both capture social signals and meet the design constraints of mobile devices. Through the analysis of several large datasets, we identify features from these sensors that correlate well with the underlying social structure of interacting groups of people. We then detail the work that we have done creating infrastructure that integrates social sensors into social applications that run on mobile devices.
by Jonathan Peter Gips.
S.M.

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Boussemart, Yves 1980. "Predictive models of procedural human supervisory control behavior." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/79543.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2011.
Page 150 blank. Cataloged from PDF version of thesis.
Includes bibliographical references (p. 138-149).
Human supervisory control systems are characterized by the computer-mediated nature of the interactions between one or more operators and a given task. Nuclear power plants, air traffic management and unmanned vehicles operations are examples of such systems. In this context, the role of the operators is typically highly proceduralized due to the time and mission-critical nature of the tasks. Therefore, the ability to continuously monitor operator behavior so as to detect and predict anomalous situations is a critical safeguard for proper system operation. In particular, such models can help support the decision making process of a supervisor of a team of operators by providing alerts when likely anomalous behaviors are detected. By exploiting the operator behavioral patterns which are typically reinforced through standard operating procedures, this thesis proposes a methodology that uses statistical learning techniques in order to detect and predict anomalous operator conditions. More specifically, the proposed methodology relies on hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) to generate predictive models of unmanned vehicle systems operators. Through the exploration of the resulting HMMs in two distinct single operator scenarios, the methodology presented in this thesis is validated and shown to provide models capable of reliably predicting operator behavior. In addition, the use of HSMMs on the same data scenarios provides the temporal component of the predictions missing from the HMMs. The final step of this work is to examine how the proposed methodology scales to more complex scenarios involving teams of operators. Adopting a holistic team modeling approach, both HMMs and HSMMs are learned based on two team-based data sets. The results show that the HSMMs can provide valuable timing information in the single operator case, whereas HMMs tend to be more robust to increased team complexity. In addition, this thesis discusses the methodological and practical limitations of the proposed approach notably in terms of input data requirements and model complexity. This thesis thus provides theoretical and practical contributions by exploring the validity of using statistical models of operators as the basis for detecting and predicting anomalous conditions.
by Yves Boussemart.
Ph.D.

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Gu, Chunju. "Experimental Evaluation of Multiscale Behavior of Human Bone." Diss., North Dakota State University, 2015. http://hdl.handle.net/10365/24781.

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Gu, Chunju. "Experimental Evaluation of Multiscale Behavior of Human Bone." Diss., North Dakota State University, 2014. https://hdl.handle.net/10365/27606.

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Bone is the most important structural member of the human body. It has a unique hierarchical structure and its primary constituents, collagen molecules and hydroxyapatite, are arranged in a staggered pattern at nanometer scale. Osteogenesis imperfecta (OI) is an inheritable disease characterized by the fragility of bones and other tissues rich in the type I collagen. OI provides an interesting platform for investigating how alterations of collagen at the molecular level cause changes in the structure of bone. In this dissertation, multi-scale-, particularly nanometer and sub-micro scale-, behaviors of both normal and OI (putative type I) human bones have been evaluated experimentally. Since chemical treatment influences collagen or mineral structure, we have used ?undisturbed bone samples? that are not subjected to any chemicals as previously done in literature. Photoacoustic-Fourier transform infrared spectroscopy (PA-FTIR) experiments reveal orientational differences in stoichiometry of hydroxyapatite. FTIR, electron microscopy, scanning probe microscopy, and nanomechanical tests also show that the OI disease results in a distorted microstructure in bone and that the mineralization of hydroxyapatite in OI is also altered. Modulus mapping test displays the distribution of mineralized fibril and extrafibrillar mineral according to the spatial variation of elastic properties. Dynamic nanomechanical behaviors of OI bone and normal bone indicates that the viscoelasticity of intact bone is mostly determined by the mineral. Also investigated are molecular composition and nanomechanical properties of different anatomical positions in the diaphysis of an OI human tibia. Our study on OI bone describes unique differences in collagen as previously described but also elaborates on unique influence of the non-collagenous proteins on mineralization of bone in OI. The fundamental premise of this work is investigation of the molecular basis of this highly debilitating bone disease.
North Dakota State University (NDSU, Doctoral Dissertation Fellowship)
National Science Foundation (NSF MRI)
National Science Foundation (NSF IMR)
Department of Civil Engineering. North Dakota State University
USDA
ND EPSCoR

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Atmakur, Sruthi. "Research in Public Spaces: Safety and Human Behavior." Thesis, Virginia Tech, 2010. http://hdl.handle.net/10919/31258.

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This thesis is a study of public spaces with a focus on personal safety and human behavior in public spaces. It establishes literature in the realm of public spaces, safety standards, and behavioral research and aims to identify common ground or conflicts between people behavior in public spaces and safety standards of public spaces. Research is supported through detailed on-site analysis and various techniques of behavioral research of two plazas in a campus setting.

The first part of the research focuses on literature to understand origin of public space, importance of safety, and evolution of safety standards in the context of Crime Prevention Through Environmental Design (CPTED). The later part of this thesis is based on preliminary site investigations, which help in identifying two public plazas on the Virginia Tech campus to provide a platform to conduct research and help identify common grounds or conflicts between safety standards and human behavior. The research also aims to help revise techniques of safety evaluation of public spaces, based on human needs and behavior. The research is primarily qualitative in nature supported with a concise quantitative data analysis to ascertain participant demographics and social needs.
Master of Landscape Architecture

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Singh, Meghendra. "Human Behavior Modeling and Calibration in Epidemic Simulations." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/87050.

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Human behavior plays an important role in infectious disease epidemics. The choice of preventive actions taken by individuals can completely change the epidemic outcome. Computational epidemiologists usually employ large-scale agent-based simulations of human populations to study disease outbreaks and assess intervention strategies. Such simulations rarely take into account the decision-making process of human beings when it comes to preventive behaviors. Absence of realistic agent behavior can undermine the reliability of insights generated by such simulations and might make them ill-suited for informing public health policies. In this thesis, we address this problem by developing a methodology to create and calibrate an agent decision-making model for a large multi-agent simulation, in a data driven way. Our method optimizes a cost vector associated with the various behaviors to match the behavior distributions observed in a detailed survey of human behaviors during influenza outbreaks. Our approach is a data-driven way of incorporating decision making for agents in large-scale epidemic simulations.
Master of Science
In the real world, individuals can decide to adopt certain behaviors that reduce their chances of contracting a disease. For example, using hand sanitizers can reduce an individual‘s chances of getting infected by influenza. These behavioral decisions, when taken by many individuals in the population, can completely change the course of the disease. Such behavioral decision-making is generally not considered during in-silico simulations of infectious diseases. In this thesis, we address this problem by developing a methodology to create and calibrate a decision making model that can be used by agents (i.e., synthetic representations of humans in simulations) in a data driven way. Our method also finds a cost associated with such behaviors and matches the distribution of behavior observed in the real world with that observed in a survey. Our approach is a data-driven way of incorporating decision making for agents in large-scale epidemic simulations.

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Zhao, Hongyang. "Motion Sensors-Based Human Behavior Recognition And Analysis." W&M ScholarWorks, 2020. https://scholarworks.wm.edu/etd/1593091889.

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Human behavior recognition and analysis have been considered as a core technology that can facilitate a variety of applications. However, accurate detection and recognition of human behavior is still a big challenge that attracts a lot of research efforts. Among all the research works, motion sensors-based human behavior recognition is promising as it is low cost, low power, and easy to carry. In this dissertation, we use motion sensors to study human behaviors. First, we present Ultigesture (UG) wristband, a hardware platform for detecting and analyzing human behavior. The hardware platform integrates an accelerometer, gyroscope, and compass sensor, providing a combination of (1) fully open Application Programming Interface (API) for various application development, (2) appropriate form factor for comfortable daily wear, and (3) affordable cost for large scale adoption. Second, we study the hand gesture recognition problem when a user performs gestures continuously. we propose a novel continuous gesture recognition algorithm. It accurately and automatically separates hand movements into segments, and merges adjacent segments if needed, so that each gesture only exists in one segment. Then, we apply the Hidden Markov Model to classify each segment into one of predefined hand gestures. Experiments with human subjects show that the recognition accuracy is 99.4% when users perform gestures discretely, and 94.6% when users perform gestures continuously. Third, we study the hand gesture recognition problem when a user is moving. We propose a novel mobility-aware hand gesture segmentation algorithm to detect and segment hand gestures. We also propose a Convolutional Neural Network to classify hand gestures with mobility noises. For the leave-one-subject-out cross-validation test, experiments with human subjects show that the proposed segmentation algorithm achieves 94.0% precision, and 91.2% recall when the user is moving. The proposed hand gesture classification algorithm is 16.1%, 15.3%, and 14.4% more accurate than state-of-the-art work when the user is standing, walking, and jogging, respectively. Finally, we present a tennis ball speed estimation system, TennisEye, which uses a racket-mounted motion sensor to estimate ball speed. We divide the tennis shots into three categories: serve, groundstroke, and volley. For a serve, we propose a regression model to estimate the ball speed. In addition, we propose a physical model and a regression model for both groundstroke and volley shots. Under the leave-one-subject-out cross-validation test, evaluation results show that TennisEye is 10.8% more accurate than the state-of-the-art work.

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Zhang, Xingye. "A SUBSYSTEM IDENTIFICATION APPROACH TO MODELING HUMAN CONTROL BEHAVIOR AND STUDYING HUMAN LEARNING." UKnowledge, 2015. http://uknowledge.uky.edu/me_etds/70.

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Humans learn to interact with many complex dynamic systems such as helicopters, bicycles, and automobiles. This dissertation develops a subsystem identification method to model the control strategies that human subjects use in experiments where they interact with dynamic systems. This work provides new results on the control strategies that humans learn. We present a novel subsystem identification algorithm, which can identify unknown linear time-invariant feedback and feedforward subsystems interconnected with a known linear time-invariant subsystem. These subsystem identification algorithms are analyzed in the cases of noiseless and noisy data. We present results from human-in-the-loop experiments, where human subjects in- teract with a dynamic system multiple times over several days. Each subject’s control behavior is assumed to have feedforward (or anticipatory) and feedback (or reactive) components, and is modeled using experimental data and the new subsystem identifi- cation algorithms. The best-fit models of the subjects’ behavior suggest that humans learn to control dynamic systems by approximating the inverse of the dynamic system in feedforward. This observation supports the internal model hypothesis in neuro- science. We also examine the impact of system zeros on a human’s ability to control a dynamic system, and on the control strategies that humans employ.

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Prykhodko, I. "Influence of design and its methods on the human subconscious." Thesis, Київський національний університет технологій та дизайну, 2018. https://er.knutd.edu.ua/handle/123456789/11423.

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Hayes, Kate. "A Qualitative Analysis of Student Behavior and Language During Group Problem Solving." Fogler Library, University of Maine, 2009. http://www.library.umaine.edu/theses/pdf/HayesK2009.pdf.

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Wells, Cathy Clarke. "The complex spatial topography of visual attention : behavior and physiology /." View online version; access limited to Brown University users, 2005. http://wwwlib.umi.com/dissertations/fullcit/3174692.

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Lannie, Amanda L. "Increasing the effectiveness of self-monitoring programs a sequenced approach with performance feedback to monitor on-task behavior and math performance /." Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2006. http://proquest.umi.com/login?COPT=REJTPTU0NWQmSU5UPTAmVkVSPTI=&clientId=3739.

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Rogers, Anthony Jerome. "Psychology as critical dialogue : a comparison of monological and dialogical views of human action /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Hannula, Gustaf. "Monkey see, monkey do? An intercultural exploration of the dynamics between humans and non-human primates in a professional animal research setting." Scholarly Commons, 2007. https://scholarlycommons.pacific.edu/uop_etds/677.

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This thesis is an exploration of the perceptions of a group of humans in interaction with a group of non-human primates in a professional animal research setting. The study is a novel investigation in the field of intercultural relations, exploring the values and beliefs of a group of research employees, and the intercultural competence and sensitivity these employees model in their interactions with the animals they work with. A focus group was conducted at the Oregon National Primate Research Center and 8 individuals working with non-human primates were interviewed. They were asked a series of 15 open-ended questions in order to explore their identification and appreciation of cultural differences, as well as their general strategies for adapting to cultural difference in the context of an animal research setting. The results of this meeting reflect a range of perceptions, attitudes, and beliefs relative to culture and the possibility of an intercultural relationship between species.

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Song, Lulu. "Infants' categorization of intransitive human actions." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 146 p, 2009. http://proquest.umi.com/pqdweb?did=1885693311&sid=6&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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Lichliter, Ann. "Organizational commitment of Nonprofit Human Service assistants." Thesis, Saint Mary's University of Minnesota, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=3745603.

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Nonprofit Human service assistants provide much of the direct care for the most vulnerable and disenfranchised populations (Annie E. Casey Foundation, 2003; Cleary et al., 2006.) Their high turnover rates compromise service quality and increase nonprofit costs (Annie E. Casey Foundation, 2003; Baumeister & Zaharia, 1987; Durlak & Roth, 1983; Mor Barak, Nissly, & Levin, 2001; Rutowski, Guiler, & Schimmel, 2009). To ensure client services are effective, nonprofit leaders need to identify strategies to enhance human service assistant’s organizational commitment. This qualitative study explored how human service assistants perceived their organizational commitment and the experiences that impacted their organizational commitment. From the interviews with 21 human service assistants, a grounded theory emerged illustrating the factors that foster organizational commitment. Human service assistants arrived at their agency with experiences, characteristics, and/or personal circumstances that may have influenced their commitment. Once employed, the nonprofit environment provided experiences that fostered organizational commitment. The culmination of these experiences resulted in participants feeling valued by the organization or I matter. Feeling they mattered was the core condition for organizational commitment.

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Minott, Sandra Simone. "Validation of Parsons' Structural Functionalism Theory Within a Multicultural Human." ScholarWorks, 2016. https://scholarworks.waldenu.edu/dissertations/3357.

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Shared values and norms are at the core for unifying different cultures socializing or working to fulfill the goals and mission of organizations. Researchers have not examined how employees representing different cultures socialize via shared norms and values in human service companies. The purpose of this ethnography study was to explore the process of 8 culturally different employees working together at a human service organization. Using purposeful sampling, multicultural employees were selected from 4 departments within the human service company. Face-to-face interviews, field notes, questionnaire, and participant observation were the tools for collecting the data. Descriptive coding, value coding, and the Ethnograph software was used to identify themes from the data. The analysis of the data evolved from using the approach of the hermeneutic circle, which consisted of examining the parts, such as activities and the connection to the whole, such as core values. According to the study, most employees engaged in sharing the norms and values of the human service company, ultimately fulfilling the goals or core values. However, 2 out of 8 participants engaged in conflict and had a lack of knowledge about 1 out of 4 core values. Knowledge and compliance to the core values were fundamental ingredients for providing quality services. This study leads to positive social change by providing human service organizations information on compliance to the entire core values of the human service company and knowledge of the complete core values of the human service company.

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Cho, Seung Bin. "Decomposing individual and group differences of categorical variables with genetic factor model." Diss., Columbia, Mo. : University of Missouri-Columbia, 2007. http://hdl.handle.net/10355/4926.

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Thesis (M.A.)--University of Missouri-Columbia, 2007.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on January 3, 2008) Includes bibliographical references.

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Baiget, Arasanz Pau. "Modeling Human Behavior for Image Sequence Understanding and Generation." Doctoral thesis, Universitat Autònoma de Barcelona, 2009. http://hdl.handle.net/10803/5791.

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La comprensió del comportament dels animals, i en especial dels humans, és un dels problemes més antics i estudiats al llarg de la història, quasi des del inici de la civilització. La quantitat de factors diferents que actuen alhora de determinar les accions d'una persona requereixen la participació de diferents disciplines, com la psicologia, biologia, o sociologia. En els darrers anys l'anàlisi del comportament humà ha esdevingut també un tema molt interessant per a la comunitat científica de visió per computador, gràcies als darrers avenços en l'adquisició de dades sobre el moviment humà a partir de seqüències d'imatges.
Malgrat la creixent disponibilitat d'aquestes dades, existeix encara una barrera per obtenir una representació conceptual de les observacions obtingudes. L'avaluació del comportament humà en seqüències d'imatges està basat en una interpretació qualitativa dels resultats, i per tant l'assignació de conceptes a les dades quantitatives obtingudes està lligada a una certa ambigüitat.
Aquesta Tesi confronta el problema d'obtenir una representació correcta del comportament humà en els contexts de la visió i animació per computador. En primer lloc, un bon model de comportament ha de permetre reconèixer i descriure l'activitat observada en seqüències d'imatges. D'altra banda, el model ha de permetre generar sintèticament noves instàncies, que permetin modelar el comportament d'agents virtuals.
En primer lloc, proposem mètodes per aprendre els models directament de les observacions. A partir de les dades obtingudes mitjançant l'anàlisi de seqüències d'imatges, construïm models de comportament normal dins l'escenari. Això ens proporciona una eina per determinar la normalitat o anormalitat de futures observacions. Tanmateix, els mètodes d'aprenentatge automàtic son incapaços de proveir una descripció semàntica de les observacions. Aquesta problema és tractat mitjançant un nou mètode que incorpora un coneixement a--priori sobre l'escena i els comportaments esperats. Aquesta estructura, formada pel motor de raonament difús FMTL i l'eina de modelatge SGT, permet obtenir descripcions conceptuals del contingut de noves seqüències de vídeo. Finalment, l'estructura FMTL + SGT ens permet simular comportament sintètic i introduir agents virtuals dins d'escenes reals que interactuen amb els agents reals existents, construint d'aquesta manera seqüències de realitat augmentada.
El conjunt de mètodes presentats en aquesta Tesi tenen un conjunt potencial d'aplicacions cada cop més gran. Per un costat, el reconeixement i descripció de comportament en seqüències d'imatges té com a principal aplicació la vídeo--vigilància intel·ligent, permetent detectar comportaments delictius o perillosos. Altres aplicacions inclouen la transcripció d'esdeveniments esportius, monitorització de centres geriàtrics, anàlisi de tràfic en carreteres i la construcció de buscadors de vídeo basats en conceptes semàntics. D'altra banda, l'animació d'agents virtuals amb comportaments complexes permet obtenir simulacions acurades de situacions reals, com per exemple incendis o multituds. A més, la inclusió d'agents virtuals en entorns reals té forta implantació en els mons dels videojocs i el cinema.
The comprehension of animal behavior, especially human behavior, is one of the most ancient and studied problems since the beginning of civilization. The big list of factors that interact to determine a person action require the collaboration of different disciplines, such as psichology, biology, or sociology. In the last years the analysis of human behavior has received great attention also from the computer vision community, given the latest advances in the acquisition of human motion data from image sequences.
Despite the increasing availability of that data, there still exists a gap towards obtaining a conceptual representation of the obtained observations. Human behavior analysis is based on a qualitative interpretation of the results, and therefore the assignment of concepts to quantitative data is linked to a certain ambiguity.
This Thesis tackles the problem of obtaining a proper representation of human behavior in the contexts of computer vision and animation. On the one hand, a good behavior model should permit the recognition and explanation the observed activity in image sequences. On the other hand, such a model must allow the generation of new synthetic instances, which model the behavior of virtual agents.
First, we propose methods to automatically learn the models from observations. Given a set of quantitative results output by a vision system, a normal behavior model is learnt. This result provides a tool to determine the normality or abnormality of future observations. However, machine learning methods are unable to provide a richer description of the observations. We confront this problem by means of a new method that incorporates prior knowledge about the environment and about the expected behaviors. This framework, formed by the reasoning engine FMTL and the modeling tool SGT allows the generation of conceptual descriptions of activity in new image sequences. Finally, we demonstrate the suitability of the proposed framework to simulate behavior of virtual agents, which are introduced into real image sequences and interact with observed real agents, thereby easing the generation of augmented reality sequences.
The set of approaches presented in this Thesis has a growing set of potential applications. The analysis and description of behavior in image sequences has its principal application in the domain of smart video--surveillance, in order to detect suspicious or dangerous behaviors. Other applications include automatic sport commentaries, elderly monitoring, road traffic analysis, and the development of semantic video search engines. Alternatively, behavioral virtual agents allow to simulate accurate real situations, such as fires or crowds. Moreover, the inclusion of virtual agents into real image sequences has been widely deployed in the games and cinema industries.

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Amato, Roberta. "Human collective behavior models: language, cooperation and social conventions." Doctoral thesis, Universitat de Barcelona, 2018. http://hdl.handle.net/10803/565420.

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The topics dealt with in this thesis are all part of the general problem of social consensus, namely how a convention flourish and decay and what motivates people to conform to it. Examples range from driving on the right side of the street, to language, rules of courtesy or moral judgments. Some conventions arise directly from the need to coordinate or conform, such as fashion or speaking the same language, others, instead, apply to situations where there is a tension between individual and collective interest, such as cooperation, reciprocity, etc. This thesis is developed around three main questions still open in the research field of collective human behavior: how coexistence of concurrent conventions is possible, why cooperation in real systems is more common than predicted and how a population undergoes collective behavioral change, namely how an initially minority norm can supplant a majority ones. In the first work, we study the impact of concurrent social pressures in consensus processes. We propose a model of opinion competition where individuals participate in different social networks and receive conflicting social influences. The dynamics take place in two distinct domains, which we model as layers of a multiplex network. The novelty of our study lies to the fact that individuals can have different options in the different layers. This naturally reflects a common situation where an individual can possess some different opinions in different social contexts as a result of consensus with other individuals in the one context but not in the other. Our analysis shows that the latter property enriches the system’s dynamics and allows not only for consensus into a single state for both layers, but also for active dynamical states of coexistence of both options. In the second model, we analyze the influence of opinion dynamic in competitive strategical games. Cooperation between humans is quite common and stable behavior even in situations where both game theory and experiments predict defection prevalence. One of the reasons could be just the fact that individuals engaging in strategic interactions are also exposed to social influence and, consequently, to the spread of opinions. We present a new evolutionary game model where game and opinions dynamics take place in different layers of a multiplex network. We show that the coupling between the two dynamical processes can lead to cooperation in scenarios where the pure game dynamics predicts defection and, in some particular setting, gives rise to a metastable state in which nodes that adopt the same strategy self-organize into local groups. In the last work, we present the first extensive quantitative analysis of the phenomenon of norm change by looking at 2,365 orthographic and lexical norms shifts occurred in English and Spanish over the last two centuries as recorded by millions of digitized books. We are able to identify three distinct patterns in the data depending on the nature of the norm shift. Furthermore, we propose a simple evolutionary model that captures all the identified mechanisms and reproduces quantitatively the transitions between norms. This work advances the current understanding of norm shifts in language change, most often limited to qualitative illustrations (e.g., the observation that adoption curve of the new norm follows an ‘S-shaped’ behavior.
Esta tesis se desarrolla en torno a tres preguntas principales aún abiertas en el contexto del estudio de los comportamientos humanos colectivos: ¿cómo es posible la coexistencia de convenciones (opiniones, idiomas, etc. ) concurrentes?; ¿por qué la cooperación en sistemas reales es más común de lo que se predice?; y ¿cómo una norma inicialmente minoritaria puede suplantar a una mayoría? En el primer trabajo nos centramos en formular un modelo capaz de contemplar la coexistencia de convenciones opuestas como una solución dinámica estable. En el segundo modelo, analizamos la influencia de la dinámica de opinión el primer análisis cuantitativo (el mejor de nuestro conocimiento) del fenómeno de evolución de las normas, es decir, lo que sucede cuando una nueva norma social reemplaza a una norma existente. Resumiendo, los resultados obtenidos en estos trabajos muestran que al modelar los comportamientos humanos colectivos, el hecho de que los individuos participan simultáneamente en diferentes contextos sociales juega un papel importante. Esto implica que los individuos están sujetos tanto a la influencia de diferentes dinámicas sociales como a estructuras de interacciones diferentes, pero no independientes. También hemos demostrado que, en el complejo proceso de cambio colectivo en la adopción de normas, la naturaleza del cambio de normas deja patrones distintos en los datos representados por tres tipos diferentes de transición dinámica. Este último trabajo avanza la comprensión actual de la evolución de las normas, más a menudo limitado a ilustraciones cualitativas (por ejemplo, la observación de que la curva de adopción de la nueva norma sigue un comportamiento ”en forma de S” ).

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Dissertations / Theses on the topic 'Human behavior'

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