Problem Detection and Definition - The Case of Farmers' Choice of Organic Milk Production

Little is known about problem detection and definition, despite that it starts the decision making process. Problem detection means becoming aware of a problem, i.e. of a difference between a desired and perceived situation. Problem definition is the process of specifying the problem, identifying decision options and choosing options to develop further through planning and analysis. The aim is to explain problem detection and definition using the case of farmers' choice of converting to organic milk production. Literature and case studies are used to generate a hypothetical model, which is estimated with survey data, path analysis, the Maximum Likelihood estimator and structural equation modeling. Different problems were identified, such as an ideological problem, a profitability problem or a production problem. Problem detection was affected by farm size, production intensity, dependency on milk production and the financial situation. The decision options included quitting farming, quitting milk production and starting alternative production. Perceived threats concerned 'rules and bureaucracy', 'economy' and 'labor and health situation'. Perceived future opportunities included 'less rigid rules', 'economy', 'way of competing', and 'environmental and personal experiences'. Lack of data about economy and rules probably contributed to the perceived risk.problem detection, problem definition, decision making process, organic milk production, structural equation modeling, Farm Management, Livestock Production/Industries,

Automatic problem detection during audio or video call

Participants in an audio or video call over a network often face problems such as being unable to hear or see each other. With user permission, speech data from the call is automatically analyzed by a classifier to identify whether call participants are facing a problem. If a problem is identified, a message indicating a possible solution is provided, or if the call participant permits, automatic action is taken to fix the source of the problem. Users are provided with options to turn off the techniques

Acquisition Program Problem Detection Using Text Mining Methods

This research provides program analysts and Department of Defense leadership with an approach to identify problems in real-time for acquisition contracts. Specifically, we test the abilities of statistical algorithms using text mining techniques to detect unusual changes in acquisition programs’ cost estimates at the completion of the programs. Currently, the government purchases monthly written reports, an informational tool on status of an acquisition program, but has not been integrated into problem prediction analysis. We center our research on the following two questions: First, can we quantify the qualitative written reports? Second, can we use these quantifications of the texts to predict cost growths in acquisition programs? Through using text mining techniques, we validate the worth of the written reports by creating algorithms that identify 80% percent of problems in acquisition programs, while increasing the probability of a problem existing given our algorithm detects by 56% from the current methods. These positive results for this analysis provide program offices with a method to detect potential problems in acquisition contracts; furthermore, this research helps the government more efficiently manage their resources as well as reduce cost and schedule overruns

Perception of Selected Secondary School Personnel Related to Problem Detection

The problem of this study was to determine the current methodologies used by selected secondary school personnel in early problem detection as the basis for development of generic detection paradigms. The sample size of this study consisted of 48 principals, 46 guidance counselors, and 347 classroom teachers. The principal of each secondary school in the study area was selected for inclusion in the study, and the guidance department chairman of each secondary school was selected for inclusion in the study, while the teachers were randomly selected for inclusion in the study. The total group was described based on demographic data. The F test for independent samples and analysis of variances were used for statistical analysis. Twenty problems with a total of 61 indicators were analyzed by the use of mean scores for each sample group calculated for test purposes. The total mean score for each indicator was calculated to rank the indicators in the paradigms. There was general agreement among school personnel about the indicators listed as being indicative of the 20 problems listed by the jury members. It was also concluded that there was very little association between the persons\u27 job titles and their mean perceptional score on each problem

ILP Experiments in Detecting Traffic Problems

The paper describes experiments in automated acquisition of knowledge in traffic problem detection. Preliminary results show that ILP can be used to successfully learn to detect traffic problems

Applying Audio Problem Detection Algorithms to Sounds on Freesound Web Platform

With the domination of music broadcasting through the internet, detecting audio problems on sound files became more important. Essentia and other similar sound libraries enabled to detect audio problems automatically, which made the detection process faster, cheaper, and more accurate. These algorithms were mostly applied on music files which are usually recorded with adequate equipment in studio environment, and afterwards that are mixed and mastered. But there are many types of sounds other than music that can be recorded and uploaded to web. Freesound is a web platform that has currently the largest collection of sounds that are recorded in different environments, using different equipment, and tagged with suitable keywords. FSD50K is the dataset that includes 51,197 carefully annotated sounds gathered from Freesound Platform. In this study, five of the mostly used automatic audio problem detection algorithms were selected. These are discontinuity, gap, click, hum, and clipping problems. Using Essentia, these algorithms were applied on FSD50K. Some bugs in the algorithms were detected and fixed, or some limitations were identified in their usage. For each audio problem, the sound classes that have the most problematic audio file percentages were detected. Besides these already implemented algorithms, a detection algorithm for asymmetry, that is another property of sound, and which does not always have to be a problem, was implemented and results were analyzed. An audio analyzer was implemented and added to Freesound Platform that allows users to filter the sound they are looking for based on whether the sound is problematic or not, according to the audio problem detection algorithms tested in the study

Matrix based problem detection in the application of software process patterns

Software development is rarely an individual effort and generally involves teams of developers. Such col- laborations require proper communication and regular coordination among the team members. In addition, coordination is required to sort out problems due to technical dependencies that exist when components of one part of the architecture requires services or data input from components of another part of the architecture. The dynamic allocation of the different tasks to people results in various socio-technical structure clashes (STSCs). These STSCs become more pronounced in an Agile Software Development environment and managerial intervention is constantly required to alleviate problems due to STSCs. In this paper we propose a technique based on dependency matrices that detects STSCs in the organizational process structure. We illustrate this technique using two examples from Organizational and Process Pattern literature

Automatic Web Navigation Problem Detection Based on Client-Side Interaction Data

The current importance of digital competence makes it essential to enable people with disabilities to use digital devices and applications and to automatically adapt site interactions to their needs. Although most of the current adaptable solutions make use of predefined user profiles, automatic detection of user abilities and disabilities is the foundation for building adaptive systems. This work contributes to diminishing the digital divide for people with disabilities by detecting the web navigation problems of users with physical disabilities based on a two-step strategy. The system is based on web user interaction data collected by the RemoTest platform and a complete data mining process applied to the data. First, the device used for interaction is recognized, and then, the problems the user may be having while interacting with the computer are detected. Identification of the device being used and the problems being encountered will allow the most adequate adaptation to be deployed and thus make the navigation more accessible

Speeding problem detection in business surveys: benefits of statistical outlier detection methods

Speeding describes the unusually fast responses provided to survey questions. A characteristic of speeders is that answers by-pass cognitive process. Consequently, this low respondent engagement results in the poor quality and validity of data. The issue at hand is how to detect speeders in a survey. The presumption is the use of different statistical outlier detection methods. This paper presents graphical methods for outlier detection, such as: dot-plot diagrams, scatter diagrams, histograms and box-plot diagrams. Furthermore, the quantitative methods for outlier detection in this paper are the z-score, modified z-score, Dixons’ test, Grubbs’ test, Tietjen-Moore test, Rosners’ or the generalized extreme studentized deviate (ESD) test. The performance of these outlier detection methods was observed on completion times of 217 surveys from enterprises which participated in a web survey on the use of statistical methods, and which use them in their business processes. The analysis has shown that none of the observed outlier detection methods were able to detect speeders in an appropriate and satisfactory way as shown by the threshold. The main reasons for this can be found in slowers, the violations of assumptions on normal distribution and in masking. Hence, existing outlier detection methods should be improved and adjusted in future research in order to detect speeders. The introduction of novel speeders detection methods would be a good choice for future research