With the increased use of online recruitment websites, many organizations have started building up these services to serve various kinds of users. There are some forms of usability evaluations that measure web applications. The inspection method is the most widely used, especially the heuristics evaluation. However, when facing substantial market demands, there is no set of heuristics to guide web designers on how to offer users a good user experience.  Therefore, developing usability heuristics for online recruiting websites is very necessary. According to existing research, Nielsen ten heuristics are the most widely used and well known. However, with the continual emergence of new technologies, these ten heuristics cannot continue to satisfy designers who are looking for more focused heuristics that apply to a specific domain. There exists no working strategy for developing specialized guidelines to ensure specialized heuristics actually work better than using general heuristics.  This thesis aims to develop usability heuristics for online recruitment website domains. Even though there are no appropriate theories or models for developing heuristics in a specific domain, we established a proposal of 21 usability heuristics for online recruitment based on the existing heuristics and methodologies to develop new specialized heuristics. These new heuristics offer evaluators smoother operations with higher efficiency (problems/time spent) in heuristics evaluation.

This commentary reflects on a series of published research articles, 1996-2013, that form a PhD thesis by publication. The articles offer evidence of research into best practice relating to Assistive Technology (AT) product design as a specialist section of Industrial Design (ID). The aim of the research has been to provide AT product developers with a methodology that ordered and prioritised the application of proven research methods, design methods and design heuristics; as well as, to highlight the fundamental concepts that underpin the methodology. This commentary provides a review of the methods applied and discussion of their efficacy within each case study. The series of articles, evaluated at a meta-analysis level in the second part of the commentary, address the following research questions: 1) What is the optimum order and priority of conventional design methods, heuristics and research methods when applied within a new product development process for assistive technology products?, 2) Through a meta-analysis of case studies, are there key aspects that underpin an optimum AT-ID process? From the review, 61 research methods, design methods and heuristics were defined. An order of methods and heuristics identified some methods that were used throughout all phases of a NPD process that included literature review, benchmarking, mixed methods and participatory research. The methods and heuristics used in all phases highlighted a user-centred approach and the close collaboration with end users and stakeholders. There was also a focus of methods and heuristics around phase 2 of the 5 design process phases defined by Martin and Hannington. The critical review also highlighted key underpinning aspects that helped optimise an Industrial Design approach to ID-AT NPD. These were 1) creating a format for dialogue within the constraints of perception and 2) previous experience and the application of ethically sound protocols for the whole process. Lastly the change of terminology and attitudes of those working the Assistive Technology industry highlighted the need for more research into social acceptance of all aspects of Assistive Technology and the perception of disability from those living with impairment and by UK society as a whole.

Esta tese investiga heurísticas de criação de oportunidades empreendedoras em startups. Pesquisas sobre ação empreendedora com base em heurísticas são desenvolvidas tendo como premissa que o mercado oferece várias oportunidades, prontas para serem selecionadas. Tais pesquisas não investigam como se dá a ação empreendedora quando há a necessidade de criação de uma nova oportunidade. Pesquisas sobre ação empreendedora com base em heurísticas também não exploram conceitualmente e empiricamente a criação de oportunidades empreendedoras por startups. Para preencher essas lacunas, esta tese se propõe a analisar a aderência da tipologia proposta por Bingham e Eisenhardt (2011) na criação de oportunidades empreendedoras por startups. A proposição que se faz é a seguinte: a tipologia proposta por Bingham e Eisenhardt não prevê heurísticas de gestão para a criação de novas oportunidades, já que tem como premissa que o mercado dispõe de várias oportunidades prontas para serem capturadas. Com base nesta análise, a seguinte pergunta dirige esta tese: Quais as heurísticas presentes na criação de oportunidades empreendedoras? Para responder a esta pergunta, esta tese constrói um quadro conceitual a partir da literatura e emprega a abordagem de pesquisa empírica, através da análise de ações empreendedoras unindo percepções, decisões e ações através de estudos de múltiplos casos realizados em oito startups. A contribuição central desta tese é a proposição de uma tipologia de heurísticas relacionadas à criação de oportunidades empreendedoras, adaptando a tipologia proposta por Bingham e Eisenhardt. Enquanto a tipologia de Bingham e Eisenhardt determina que empresas aprendem heurísticas dos tipos seleção, processual, prioridade e temporal, esta tese determina que empresas também aprendem heurísticas do tipo \'criação\' e propõe uma nova tipologia de heurísticas para a criação de oportunidades empreendedoras: \'startups aprendem heurísticas dos tipos criação, processual, prioridade e temporal\', uma vez que necessitam criar uma oportunidade que o mercado ainda não dispõe. / This thesis investigates heuristics creating entrepreneurial opportunities for startups. Research on entrepreneurial action based on heuristics are developed with the premise that the market offers several opportunities, ready to be selected. Such surveys do not investigate how is the entrepreneurial action when there is a need to create a new opportunity. Research on entrepreneurial action based on heuristics did not explore conceptually and empirically creating entrepreneurial opportunities for startups. To fill these gaps, this thesis aims to analyze the adherence of the typology proposed by Bingham and Eisenhardt (2011) in creating opportunities for entrepreneurial startups. The proposition that does is the following: the typology proposed by Bingham and Eisenhardt does not provide management of heuristics to create new opportunities, as it is premised that the market offers many opportunities ready to be captured. Based on this analysis, the following question directs this thesis: What heuristics present at the creation of entrepreneurial opportunities? To answer this question, this thesis builds a conceptual framework from the literature and employs empirical research approach through the analysis of entrepreneurial activities linking perceptions, decisions and actions through multiple cases performed in eight startups studies. The main contribution of this thesis is to propose a typology of heuristics related to creating entrepreneurial opportunities, adapting the typology proposed by Bingham and Eisenhardt. While the typology of Bingham and Eisenhardt requires that companies learn heuristics types selection, procedure, priority and time, this thesis requires that companies also learn heuristics like \'creation\' and proposes a new typology of heuristics to create opportunities entrepreneurial: \'startups learn heuristics types creation, procedure, priority and time\', since they need to create an opportunity that the market does not yet have.

In the health-informatics and bio-medical domains, clinicians produce an enormous amount of data which can be complex and high in dimensionality. This scenario includes visual field data, which are used for managing the second leading cause of blindness in the world: glaucoma. Visual field data are the most common type of data collected to diagnose glaucoma in patients, and usually the data consist of 54 or 76 variables (which are referred to as visual field locations). Due to the large number of variables, the six nerve fiber bundles (6NFB), which is a collection of visual field locations in groups, are the standard clusters used in visual field data to represent the physiological traits of the retina. However, with regard to classification accuracy of the data, this research proposes a technique to find other significant spatial clusters of visual field with higher classification accuracy than the 6NFB. This thesis presents a novel clustering technique, namely, Simultaneous Modelling and Clustering (SMC). SMC performs clustering on data based on classification accuracy using heuristic search techniques. The method searches a collection of significant clusters of visual field locations that indicate visual field loss progression. The aim of this research is two-fold. Firstly, SMC algorithms are developed and tested on data to investigate the effectiveness and efficiency of the method using optimisation and classification methods. Secondly, a significant clustering arrangement of visual field, which highly interrelated visual field locations to represent progression of visual field loss with high classification accuracy, is searched to complement the 6NFB in diagnosis of glaucoma. A new clustering arrangement of visual field locations can be used by medical practitioners together with the 6NFB to complement each other in diagnosis of glaucoma in patients. This research conducts extensive experiment work on both visual field and simulated data to evaluate the proposed method. The results obtained suggest the proposed method appears to be an effective and efficient method in clustering visual field data and 3 improving classification accuracy. The key contributions of this work are the novel model-based clustering of visual field data, effective and efficient algorithms for SMC, practical knowledge of visual field data in the diagnosis of glaucoma and the presentation a generic framework for modelling and clustering which is highly applicable to many other dataset/model combinations.

Numeric estimates are influenced by a variety of factors including a person's knowledge and the presence of numeric anchors. In general, greater knowledge leads to more accurate estimates and the presence of anchors decreases accuracy. This dissertation is focused on the relationship between these two factors. At an intuitive level, it seems that increased knowledge should lead to a decrease in anchoring effects. Unfortunately, the research on knowledge and anchoring is quite mixed. This dissertation describes four studies--the first three were experimental and the last was correlational--that addressed two primary questions: 1) Does knowledge level moderate anchoring effects such that greater knowledge in a domain is associated with smaller anchoring effects? 2) Does this relationship depend on the type of knowledge one has? Studies 1 and 2 provided an answer to the first question. In Study 1, participants who studied a list of country populations--i.e., high knowledge participants--were less influenced by anchors than participants who learned irrelevant information. In Study 2, those participants who studied a list of new car prices were less influenced by anchors than participants who learned irrelevant information. In Study 3, participants learned information designed to influence different types of knowledge. The results of Study 3 supported the prediction that only those participants in conditions that increased metric knowledge--and not mapping knowledge--would exhibit reduced anchoring effects. Finally, in Study 4, participants' knowledge was measured and compared to their anchoring effects. Contrary to expectations, none of the knowledge measures were related to the participants' anchoring effects. Theoretical and practical implications, as well as reasons why the last study was not consistent with the first three, are discussed. Taken together, these studies indicate that both the amount and type of knowledge one has are important in determining one's susceptibility to anchoring effects.

This research project will explore American philosopher John Dewey’s theory of transaction, and Shannon Sullivan’s interpretation of Dewey’s theory, the ‘Transactional Body’, and their inherent potential for the making and reception of painting. Dewey stated (Dewey, cited in Sullivan: 1-2) that organisms live as much in processes across and 'through' skins as in processes 'within skins'. Sullivan’s ‘transactional body’ is always in a state of flux, a morphic body in perpetual motion. Within an artistic context this raises the possibility of exploring the theory of transaction as it applies to painting, using the concept of automatic intuitive art practice. Central to this investigation will be direct connection between senses, instincts, intuition, and the painting. Sullivan suggests that truth and wisdom can be pursued through somatic experience. Therefore the process will be explored by an extension of the corporeal body through the physicality of gesture, movement, rhythm, colour, and mark making. A recurring subtext throughout this investigation will be that of ‘duality’; specifically that defined as the struggle between the use of the conscious, critical mind, allowing for the transaction between artist, paint and canvas to occur naturally and intuitively. The conscious mind / intuition duality manifests at various stages during the manufacture and reception of a painting. Whilst the project relies upon automatic and intuitive praxis, conscious decisions are made regarding the size and shape of the canvas, the medium used, and through reflective analysis of the completed work.

<p>Stock recommendations,frequently produced under time pressure, are susceptible to being the result of automatic and intuitive thinking. This is associated with using heuristics in decision-making which is studied by an entire school of research – the heuristics and bias approach. Heuristics of representativeness, availability and anchoring including associated biases as defined by Tversky and Kahneman provide the theoretical framework for the study. This study is aimed at extending the understanding of biases in general and cognitive biases with regard to stock recommendations. A total of thirty equity recommendations were analyzed. A t-test showed that more biases were present in the incorrect recommendations. Overconfidence, illusion of validity and anchoring were among the most frequently observed. The vast majority of recommendations were characterized by insensitivity to predictability indicating that forecasters are seemingly unaware of the difficulty of accurately predicting where the stock price is going to be within the next three to six months.</p>

<p><p>The use of examples is known to be important in learning, they should be “exemplary” and function as role-models.</p><p>Teaching and learning problem solving and programming in the object oriented paradigm is recognised as difficult. Object orientation is designed to handle complexity and large systems, and not with education in focus. The fact that object orientation often is used as first paradigm makes the design of examples even more difficult and important.</p><p>In this thesis, a survey of the literature is made to establish a set of characteristics for object orientation in general. This set of characteristics is then applied to the educational setting of introducing novices to object oriented problem solving and programming, resulting in a number of heuristics for educational purposes, called He[d]uristics. The proposed He[d]uristics are targeted towards educators designing small-scale examples for novices, and is an attempt to provide help in designing suitable examples, not a catalogue of good ones.</p><p>The He[d]uristics are discussed and exemplified and also evaluated versus the derived set of characteristics and known common problems experienced by novices.</p></p>

Disruptions to job shop schedules are tedious and difficult to incorporate after the schedule has been generated and implemented on the shop floor. In order to deal with such disruptions, a real time reactive scheduling strategy is essential. Reactive scheduling is the process of repairing the predictive schedule during online execution for internal disruptions (e.g. machine breakdowns) and external deviations (e.g. prepone or postpone of orders). Existing approaches for schedule repair in real time mainly utilize heuristics such as Right Shift Rescheduling (RSR), and Affected Operation Rescheduling (AOR). In the present form, both these approaches are only used for handling machine breakdowns in the shop floor, but are inept in accommodating new and unexpected job orders. These approaches also neglect specific issues related to urgent jobs, for instance multiple job routings during the repair of the schedule. In this paper the existing heuristics (RSR and AOR) have been modified to include urgent jobs. Also a modified AOR approach (mAOR) is proposed that considers urgent jobs with multiple job routings. An extensive simulation study has been conducted on a job shop simulation testbed for the efficiency and stability of the repaired schedule using the mAOR and RSR heuristics. The efficiency of the repaired schedule is a measure of the percentage change in the makespan after incorporating repairs whereas the stability of the schedule is a function of starting time deviations that indicate the degree by which it deviates from the original schedule. The results of the experiments indicate significant benefits of the modified AOR algorithm over the existing RSR schedule repair heuristic. / Singapore-MIT Alliance (SMA)

We investigate the use of machine learning to create effective heuristics for single-agent search. Our method aims to generate a sequence of heuristics from a given weak heuristic h{0} and a set of unlabeled training instances using a bootstrapping procedure. The training instances that can be solved using h{0} provide training examples for a learning algorithm that produces a heuristic h{1} that is expected to be stronger than h{0}. If h{0} is so weak that it cannot solve any of the given instances we use random walks backward from the goal state to create a sequence of successively more difficult training instances starting with ones that are guaranteed to be solvable by h{0}. The bootstrap process is then repeated using h{i} instead of h{i-1} until a sufficiently strong heuristic is produced. We test this method on the 15- and 24-sliding tile puzzles, the 17- , 24- , and 35-pancake puzzles, Rubik's Cube, and the 15- and 20-blocks world. In every case our method produces heuristics that allow IDA* to solve randomly generated problem instances quickly with solutions very close to optimal. The total time for the bootstrap process to create strong heuristics for large problems is several days. To make the process efficient when only a single test instance needs to be solved, we look for a balance in the time spent on learning better heuristics and the time needed to solve the test instance using the current set of learned heuristics. %We use two threads in parallel, We alternate between the execution of two threads, namely the learning thread (to learn better heuristics) and the solving thread (to solve the test instance). The solving thread is split up into sub-threads. The first solving sub-thread aims at solving the instance using the initial heuristic. When a new heuristic is learned in the learning thread, an additional solving sub-thread is started which uses the new heuristic to try to solve the instance. The total time by which we evaluate this process is the sum of the times used by both threads up to the point when the instance is solved in one sub-thread. The experimental results of this method on large search spaces demonstrate that the single instance of large problems are solved substantially faster than the total time needed for the bootstrap process while the solutions obtained are still very close to optimal.