Thesis (Ph.D.)--Brown University, 2005. / Vita. Thesis advisor: Thomas L. Dean. Includes bibliographical references (leaves 185-191). Also available online.

The expanded internet usage has resulted in an increased activity at web-based search engines. Companies are therefore devoting a large portion of their online marketing budget on Search Engine Marketing (abbreviated SEM) in order to reach potential online consumers searching for products. SEM comprises Search Engine Advertising (SEA) and Search Engine Optimization (SEO) which are two dissimilar marketing tools companies can invest in to reach the desired customer segments. It is therefore of great interest for companies in different product markets to have knowledge of which SEM strategy to utilize. The statement leads to the purpose of the thesis which is to investigate which SEM strategy is the most suitable for companies in different markets, SEA or SEO?. The purpose of the thesis is derived to the research problem: How does the search behavior of consumers differ between the two SEM tools, SEO and SEA?. Initially, in order to answer the research problem, a theoretical framework was conducted consisting of theories from previous research. To collect primary data observations of 60 test subjects was performed in accordance with the Experimental Vignette Methodology. The analysis consists of a comparison between the collected data and the theories included in the frame of reference, to identify similarities and differences. The SPSS analysis of the result revealed numerous findings such as the two-way interactions of the factors degree of involvement and the click rate of SEM, as well as the choice of either a head or a tail keyword and the degree of involvement. The analysis further revealed a three-way interaction which suggests that the degree of involvement, and the use of either a head or tail keyword affects the choice of SEM. Additionally, the result shows that customers using brands as keywords are more likely to click on an organic link rather than on a paid ad. However, when adding the factor age to the analysis the results turn insignificant. As the area of search behavior of customers using search engines is relatively scientifically unexplored, the thesis has contributed with knowledge useful for companies, marketing agencies, among others. However, due to the ongoing expansion of search engine usage, it is of great interest to conduct further research in the area to reveal additional findings.

The present experiment compared two methods of training a simple arm position using auditory feedback: capture and search. The participants were four right-handed female college students. During capture, auditory feedback was delivered by the experimenter after the participant moved along a single axis into the target position. During search, auditory feedback was produced by the computer after the participant left clicked a mouse inside the target location. The results of a multi-element design showed that participants performed more accurately during capture training than search training. Pre-training and post-training probes, during which no auditory feedback was provided, showed similar fluctuations in accuracy across probe types. A retention check, performed seven days after the final training session, showed higher accuracy scores for search than capture, across all four participants. These findings suggest that TAGteach should incorporate an approach similar to search training to improve training outcomes.

The idea for this study is based on a collaboration with Södra Skogsägarna Ekonomisk Förening, one of Sweden's leading forest industries, who wanted to investigate the possibilities of a new internal search function on its public website, primarily with the help of Azure Cognitive Search. Before and in connection with the implementation of a new search function, the following questions aimed to be answered: • RQ1: How does semantic search differ from keyword search? What are the limitations of semantic search today? • RQ2: In what ways does the user experience of the new search function differ from the old search function? To find answers to the questions, a literature study was conducted and case studies consisting of a survey among the website's visitors and two different user surveys. The literature study aimed to answer RQ1 and form a knowledge base for the design of the new search function by examining the search engine's history, the difference between a keyword-based search function and a semantic search function, and looking at how one expects today's smart search functions to develop. The survey included questions about visitors' use of the existing search function and perception of it. User survey number 1 was conducted with a select group of participants. The survey consisted of a number of tasks that would be performed using the existing search function to get a better picture of the user experience and help answering RQ2. When the new search function was ready for testing, User Survey number 2 was conducted where participants compared the old and the new search function by performing the same tasks with both solutions open in parallel windows. The study showed that the majority of the participants in the survey perceived the old search function as effective enough to make them satisfied. User survey 1 suggested that relevant results came too far down the results list or no relevant results were obtained at all. After implementing Azure Cognitive Search with a semantic feature enabled, test participants were able to ask questions in the search box and get answers directly at the top of the results list, which made the new search feature preferred over the old one. The literature study showed how keyword-based search is based on the principle of keywords and its occurrence in the searchable index, while a semantic search function tries to interpret the meaning behind the search term instead. / Idén till denna studie grundar sig ett samarbete med Södra Skogsägarna Ekonomisk Förening, en av Sveriges ledande skogsindustrier, som ville undersöka möjligheterna med en ny intern sökfunktion på sin publika webbplats, främst med hjälp av Azure Cognitive Search. Inför och i samband med implementeringen av en ny sökfunktion ville man besvara följande frågeställningar:   ·       RQ1: Hur skiljer sig semantisk sökning i jämförelse med sökning mot nyckelord (keywords)? Vilka begränsningar finns det med semantisk sökning idag?’ ·       RQ2: På vilka sätt skiljer sig användarupplevelsen av den nya sökfunktionen med semantisk funktion i jämförelse med den gamla, nyckelordsbaserade sökfunktionen?   För att söka svar på frågeställningarna gjordes en litteraturstudie samt fallstudier bestående av en enkät bland webbplatsens besökare och två olika användarundersökningar. Litteraturstudien ämnade besvara RQ1 och utgöra en kunskapsgrund inför utformningen av den nya sökfunktionen genom att undersöka sökmotorns historia, skillnaden mellan en nyckelordsbaserad sökfunktion och en semantisk sökfunktion samt se på hur man förväntar sig att dagens smarta sökfunktioner kommer att utvecklas. Enkäten innehöll frågor om besökarnas användande av den befintliga sökfunktionen och uppfattningen om den. Användarundersökning 1 utfördes med en utvald skara deltagare. Undersökningen bestod av ett antal uppgifter som skulle utföras med hjälp av den befintliga sökfunktionen för att få en bättre bild av användarupplevelsen och hjälpa till att besvara RQ2. När den nya sökfunktionen var klar för test gjordes Användarundersökning 2 där man lät deltagarna jämför den gamla och den nya sökfunktionen genom att utföra samma uppgifter med båda lösningarna parallellt.   Studien visade att majoriteten av deltagarna i enkäten upplevde den gamla sökfunktionen som tillräckligt effektiv för att göra dem nöjda. Användarundersökning 1 antydde att relevanta resultat kom för långt ner i resultatlistan eller så fick man inga relevanta resultat alls. Efter implementering av Azure Cognitive Search med en semantisk funktion påkopplad kunde testdeltagarna ställa frågor i sökrutan och få svar direkt högst upp i resultatlistan, vilket gjorde att den nya sökfunktionen föredrogs framför en gamla. Litteraturstudien visade på hur nyckelordsbaserat sök grundar sig på principen om nyckelord, keywords och dess förekomst i det sökbara indexet medan en semantisk sökfunktion försöker tolka meningen bakom söktermen i stället.

This thesis discusses a system that allows an operator to use two unmanned aerial vehicles (UAVs) to search an area. Prior work accomplished this in separate survey and search missions, and this work combines those two missions into one. The user conducts a search by selecting an area to survey, and the first drone flies above it, providing up to date information about the area. Points of interest (POI) are then marked by the user and investigated by the second drone. This system assumes a static and known obstacle map, and segmenting the environment during the missions leaves potential for future work. Both drones are equipped with cameras that stream video for the user to observe. A custom graphical user interface (GUI) was created to allow for the drones to be controlled. In addition to marking a search area and POI, the user can pause the drone and delete or add new POI to change the mission mid-flight. Both drones are commanded remotely by a ground station (GCS), leaving only low-level control to the onboard computers. This ground station uses a nearest neighbor solution to the travelling salesman problem and a wavefront path planner to create a path for the low altitude drone. The software architecture is based on the Robot Operating System (ROS), and the GCS uses the MAVLink messaging protocol to communicate with the drones. In addition to the system design, this paper discusses UAV human interaction and how it is applied to this system. / Master of Science / This thesis discusses a system that allows an operator to use two drones to search an area. Prior work accomplished this in separate survey and search missions, and this work combines those two missions into one. The user conducts a search by selecting an area to survey, and the first drone flies above it, providing up to date information about the area. Points of interest (POI) are then marked by the user and investigated by the second drone. This system assumes that obstacles in the environment are static and already known. Both drones are equipped with cameras that stream video for the user to observe. A custom graphical user interface (GUI) was created to allow for the drones to be controlled. In addition to providing the initial mission for the drones, the user can also change the mission mid-flight. Both drones are commanded remotely by a separate computer, leaving only very basic control to the drones. This ground station uses a simple path planner to create a path for the low altitude drone to avoid obstacles. The software architecture is based on the Robot Operating System (ROS), and the GCS uses the MAVLink messaging protocol to communicate with the drones. In addition to the system design, this UAV human interaction and how it is applied to this system.

A Reduced Ordered Binary Decision Diagram (BDD) is a symbolic data structure introduced to the model checking community by Bryant in 1986 to help verify properties of systems with very large state spaces. Recently, BDDs have been used in heuristic search algorithms as an approach to representing and solving search problems with very large state spaces. However, these algorithms are still not memory efficient. This thesis presents a symbolic heuristic search algorithm that uses BDDs in a memory efficient way by performing bidirectional breadthirst heuristic search. The approach is evaluated empirically against existing symbolic methods and is shown to provide a significant improvement in performance.

The flow shop scheduling problem is a classical combinatorial problem being studied for years. The focus of this research is to study two variants of the flow shop scheduling problem in order to minimize makespan by scheduling n jobs on m machines. A solution approach is developed for the modified flow shop problem with due dates and release times. This algorithm is an attempt to contribute to the limited literature for the problem. Another tabu search-based solution approach is developed to solve the classical flow shop scheduling problem. This meta-heuristic (called 3XTS) allows an efficient search of the neighboring solutions leading to a fast solution procedure. Several control parameters affecting the quality of the algorithm are experimentally tested, and certain rules are established for different problem instances. The 3XTS is compared to another tabu search method (that seems to be a champion) in terms of solution quality and computation time.

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