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Predicting with sparse data
It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show, for data from two companies, how our approach — based upon expert judgement — adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practising project manager. From this empirical work we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction
Chatbots for learning: A review of educational chatbots for the Facebook Messenger
With the exponential growth in the mobile device market over the last decade, chatbots are becoming an increasingly popular option to interact with users, and their popularity and adoption are rapidly spreading. These mobile devices change the way we communicate and allow ever-present learning in various environments. This study examined educational chatbots for Facebook Messenger to support learning. The independent web directory was screened to assess chatbots for this study resulting in the identification of 89 unique chatbots. Each chatbot was classified by language, subject matter and developer's platform. Finally, we evaluated 47 educational chatbots using the Facebook Messenger platform based on the analytic hierarchy process against the quality attributes of teaching, humanity, affect, and accessibility. We found that educational chatbots on the Facebook Messenger platform vary from the basic level of sending personalized messages to recommending learning content. Results show that chatbots which are part of the instant messaging application are still in its early stages to become artificial intelligence teaching assistants. The findings provide tips for teachers to integrate chatbots into classroom practice and advice what types of chatbots they can try out.Web of Science151art. no. 10386
A comparative study of multiple-criteria decision-making methods under stochastic inputs
This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated methods, such as technique for the order of preference by similarity to the ideal solution (TOPSIS) and Preference Ranking Organization method for enrichment evaluation (PROMETHEE), better predict the optimum design alternative
Trust Evaluation for Embedded Systems Security research challenges identified from an incident network scenario
This paper is about trust establishment and trust
evaluations techniques. A short background about trust, trusted
computing and security in embedded systems is given. An analysis
has been done of an incident network scenario with roaming
users and a set of basic security needs has been identified.
These needs have been used to derive security requirements for devices and systems, supporting the considered scenario. Using the requirements, a list of major security challenges for future research regarding trust establishment in dynamic networks have been collected and elaboration on some different approaches for future research has been done.This work was supported by the Knowledge foundation and RISE within the ARIES project
Farming Differentiation in the Rural-urban Interface of the Middle Mountains, Nepal: Application of Analytic Hierarchy Process (AHP)Modeling
This article investigates the dominant factors of farming differentiation in the rural-urban interface of the densely
populated Kathmandu Valley, using the Analytic Hierarchy Process (AHP) modeling. The rural-urban interface in the Kathmandu Valley is an important vegetable production pocket which supplies a large amount of the vegetables in the city core. While subsistence farming in the rural area is characterized by a system which integrates livestock and forestry with agriculture, the intensification in the urban fringe is characterized by triple crop rotations and market-oriented intensive vegetable production. Seven factors which were supposed to cause farming variation in the interface were incorporated in the AHP framework and then subjected to the farmers’ judgment in distinctly delineated three farming zones. These factors played crucial yet differing roles in different farming zones. Inaccessibility and use of local resources; higher yield and accessibility and agro-ecological consideration and quality production are the key impacting factors of subsistence, commercial inorganic and smallholder organic farming respectively. The quantification of such factors of farming differentiation through AHP is an important piece of information that will contribute in modeling farming in the rural-urban interface of developing countries which are characterized by a high diversity of farming practices and are undergoing a rapid
change in the land use pattern
An empirical learning-based validation procedure for simulation workflow
Simulation workflow is a top-level model for the design and control of
simulation process. It connects multiple simulation components with time and
interaction restrictions to form a complete simulation system. Before the
construction and evaluation of the component models, the validation of
upper-layer simulation workflow is of the most importance in a simulation
system. However, the methods especially for validating simulation workflow is
very limit. Many of the existing validation techniques are domain-dependent
with cumbersome questionnaire design and expert scoring. Therefore, this paper
present an empirical learning-based validation procedure to implement a
semi-automated evaluation for simulation workflow. First, representative
features of general simulation workflow and their relations with validation
indices are proposed. The calculation process of workflow credibility based on
Analytic Hierarchy Process (AHP) is then introduced. In order to make full use
of the historical data and implement more efficient validation, four learning
algorithms, including back propagation neural network (BPNN), extreme learning
machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture
model (FIGMN), are introduced for constructing the empirical relation between
the workflow credibility and its features. A case study on a landing-process
simulation workflow is established to test the feasibility of the proposed
procedure. The experimental results also provide some useful overview of the
state-of-the-art learning algorithms on the credibility evaluation of
simulation models
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