Unification of imprecise data - translation of fuzzy to multi-valued knowledge over Y-axis

Inference systems are a well-defined technology derived from knowledge-based systems. Their main purpose is to model and manage knowledge as well as expert reasoning to insure a relevant decision making while getting close to human induction. Although handled knowledge are usually imperfect, they may be treated using a non classical logic as fuzzy logic or symbolic multi-valued logic. Nonetheless, it is required sometimes to consider both fuzzy and symbolic multi-valued knowledge within the same knowledge-based system. For that, we propose in this paper an approach that is able to standardize fuzzy and symbolic multi-valued knowledge. We intend to convert fuzzy knowledge into symbolic type by projecting them over the Y-axis of their membership functions. Consequently, it becomes feasible working under a symbolic multi-valued context. Our approach provides to the expert more flexibility in modeling their knowledge regardless of their type. A numerical study is provided to illustrate the potential application of the proposed methodology.

JSON Schemas with Semantic Annotations Supporting Data Translation

As service-oriented architectures are a solution for large distributed systems, interoperability between these systems, which are often heterogeneous, can be a challenge due to the different syntax and semantics of the exchanged messages or even different data interchange formats. This paper addresses the data interchange format and data interoperability issues between XML-based and JSON-based systems. It proposes novel annotation mechanisms to add semantic annotations and complement date values to JSON Schemas, enabling an interoperability approach for JSON-based systems that, until now, was only possible for XML-based systems. A set of algorithms supporting the translation from JSON Schema to XML Schema, JSON to XML, and XML to JSON is also proposed. These algorithms were implemented in an existing prototype tool, which now supports these systems’ interoperability through semantic compatibility verification and the automatic generation of translators.

Leadership styles and employees' performance: A case of family-owned manufacturing company, Cape Coast

Purpose: This study aimed to explore the association between leadership styles and employee performance in a family-owned manufacturing business. Research methodology: For data translation and analysis, the study used a quantitative approach and a correlational design, a census technique of sampling 400 employees, an interview schedule, multiple linear regression, and the Statistical Package for Social Sciences (SPSS) 16.0 Versions. Results: Results revealed that autocratic, charismatic, and paternalistic leadership styles influence employees' performance. The result also revealed that autocratic, charismatic, and visionary leadership styles influence error reduction. Again the result shows that paternalistic and visionary leadership styles influence employees' quality of work. Limitations: The study's main weakness is that it only looked at nine specific leadership styles and their effects on employee performance. Contribution: Given this, managers should consider using leadership styles with stronger predictions in a given situation to drive employees' performance, reduce employees' errors in work and enhance employees' quality of work.

The effect of demographic factors on employees’ performance: A case of an owner-manager manufacturing firm

Purpose: The goal of this study is to look into how demographic factors influence employee performance in an owner-manager manufacturing firm. Research Methodology: The research was carried out in an owner-manager firm in Cape Coast, Ghana's central region. For data translation and analysis, a quantitative approach and a correlational study design were used, as well as a census sampling technique to sample 400 employees, an interview schedule, multiple linear regression, and the Statistical Package for Social Sciences (SPSS) 20.0 Versions. Result: The findings show that age and education have an impact on employees’ performance. The findings also revealed that age and department have an impact on employee absenteeism. Again the result shows that age, education, and tenure respectively influences employees’ output. However, some demographic factors have no relationship with employees’ performance, absenteeism, or output. Limitation: Time constraints, assumptions about the underlying theory, and the unwillingness of respondents to give out information were all limitations. Contribution: SMEs owners and managers must not overlook these elements, as they have a variety of effects on employees’ performance, how they miss work, and output levels. It is thought that paying attention to an employee's age groups, level of education, the department they work in and what transpired there, and years of experience will go a long way in assisting them in performing to a satisfactory level and increasing their performance.

RURAL DATA DIVIDES: CRITICAL FACTORS OF DIGITAL AGTECH ADOPTION ON AUSTRALIAN FARMS

This paper details a qualitative investigation of human factors relating to adoption of digital agricultural technologies on Australian farms. We employed an ‘ecosystems’ approach to undertake a case study of a cotton farm’s transition to digital farming. Interviews and participant observation were conducted across the farm’s supply chain to understand how the experiences, perceptions, and activities of different stakeholders constituted a community-level orientation to digital agriculture, which enabled and constrained on-farm adoption. Technology providers installed a variety of data-generating technologies – remote sensors, automation, satellite crop imagery, WiFi/4G connectivity, and a customised data dashboard on the farm. However, the farmers lacked digital and data literacy skills to access, manage and use data effectively and independently. Specialist expertise for data translation was required, and support and resourcing for the farmers to acquire data capabilities was limited. This ‘data divide’ between the generation and application of farm data was complicated by broader issues raised by participants about data ownership, portability, privacy, trust, liability, and sovereignty, which have been observed internationally. The paper raises questions about the level of expertise farmers should be expected to attain in the transition to digital farming, who in the ecosystem is best placed to fill this ‘data divide’, and what interventions are necessary to address significant barriers to adoption in rural communities. It also highlights a tension between farmers’ $2 as decision-makers on their own properties and their $2 on digital technologies – and the ecosystems that support uptake of digital AgTech – to inform on-farm decisions.

Ontology-based approach to data exchanges for robot navigation on construction sites

As the use of autonomous Unmanned Ground Vehicles (UGV) for automated data collection from construction projects increases, construction stakeholders have become aware of a problem with inter-disciplinary semantic data sharing and exchanges between construction and robotic. Cross-domain data translation requires detailed specifications especially when it comes to semantic data translation. Building Information Modeling (BIM) and Geographic Information System (GIS) are the two digital building technologies used to capture and store semantic information for indoor structures and outdoor environments respectively. In the absence of a standard format for data exchanges between the construction and robotic domains, the tools of both industries have yet to be integrated into a coherent deployment infrastructure. In other words, the semantics of BIM-GIS cannot be automatically integrated by the robotic platforms currently being used. To enable semantic data transfer across domains, semantic web technology has been widely used in multi-disciplinary areas for interoperability. This paves the way to smarter, quicker and more precise robot navigation on construction sites. This paper develops a semantic web ontology integrating robot navigation and data collection to convey the meanings from BIM-GIS to the robot. The proposed Building Information Robotic System (BIRS) provides construction data that are semantically transferred to the robotic platform and can be used by the robot navigation software stack on construction sites. To meet this objective, first, knowledge representation between construction and robotic domains is bridged. Then, a semantic database integrated with the Robot Operating System (ROS) is developed, which can communicate with the robot and the navigation system to provide the robot with semantic building data at each step of data collection. Finally, the BIRS proposed system is validated through four case studies.

The Effect of Uncertainty in Whole Building Simulation Models for Purposes of Generating Control Strategies

Buildings consume a significant amount of energy worldwide in maintaining comfort for occupants. Building energy management systems (BEMS) are employed to ensure that the energy consumed is used efficiently. However these systems often do not adequately perform in minimising energy use. This is due to a number of reasons, including poor configuration or a lack of information such as being able to anticipate changes in weather conditions. We are now at the stage that building behaviour can be simulated, whereby simulation tools can be used to predict building conditions, and therefore enable buildings to use energy more efficiently, when integrated with BEMS. What is required though, is an accurate model of the building which can effectively represent the building processes, for building simulation. Building information modelling (BIM) is a relatively new method of representing building models, however there still remains the issue of data translation between a BIM and simulation model, which requires calibration with a measured set of data. If there a lack of information or a poor translation, a level of uncertaintly is introduced which can affect the simulation’s ability to accurate predict control strategies for BEMS. This paper explores effects of uncertainty, by making assumptions on a building model due to a lack of information. It will be shown that building model calibration as a method of addressing uncertainty is no substitute for a well defined model.

A Durational Performance Using Visualized, Sonified, and Other Data Translation from Portable EEG Technologies in Mental and Physical Training

In the moment of complete engagement in any activity, we function without conscious thought—referred to as ‘the zone.’ Digital technologies, from mobile devices to the Internet, can be a constant source of diversion; however, can digital tools help us get into the zone more quickly rather than simply distract us? Using open-source software and hardware, I have developed a real-time data visualization and sonification that have been recorded as performances on the website Mind & Matter, the project accompanying this paper. The performances are filmed in different locations and the visualization geolocates these locations, comparing them to the cell towers within the area. The project seeks to show waves within and around our body that are normally invisible. Each performance seeks to train both my brain and body to find stillness within. The paper is informed by the communications theorists and artists studied throughout the Communications and Culture program. I seek to answer Catherine Malabou’s question of “What We Should Do with Our Brains,” and how we might find agency in our brain plasticity though technological extension.

A Durational Performance Using Visualized, Sonified, and Other Data Translation from Portable EEG Technologies in Mental and Physical Training

In the moment of complete engagement in any activity, we function without conscious thought—referred to as ‘the zone.’ Digital technologies, from mobile devices to the Internet, can be a constant source of diversion; however, can digital tools help us get into the zone more quickly rather than simply distract us? Using open-source software and hardware, I have developed a real-time data visualization and sonification that have been recorded as performances on the website Mind & Matter, the project accompanying this paper. The performances are filmed in different locations and the visualization geolocates these locations, comparing them to the cell towers within the area. The project seeks to show waves within and around our body that are normally invisible. Each performance seeks to train both my brain and body to find stillness within. The paper is informed by the communications theorists and artists studied throughout the Communications and Culture program. I seek to answer Catherine Malabou’s question of “What We Should Do with Our Brains,” and how we might find agency in our brain plasticity though technological extension.

OME-NGFF: scalable format strategies for interoperable bioimaging data

Biological imaging is one of the most innovative fields in the modern biological sciences. New imaging modalities, probes, and analysis tools appear every few months and often prove decisive for enabling new directions in scientific discovery. One feature of this dynamic field is the need to capture new types of data and data structures. While there is a strong drive to make scientific data Findable, Accessible, Interoperable and Reproducible (FAIR, 1), the rapid rate of innovation in imaging impedes the unification and adoption of standardized data formats. Despite this, the opportunities for sharing and integrating bioimaging data and, in particular, linking these data to other "omics" datasets have never been greater; therefore, to every extent possible, increasing "FAIRness" of bioimaging data is critical for maximizing scientific value, as well as for promoting openness and integrity. In the absence of a common, FAIR format, two approaches have emerged to provide access to bioimaging data: translation and conversion. On-the-fly translation produces a transient representation of bioimage metadata and binary data but must be repeated on each use. In contrast, conversion produces a permanent copy of the data, ideally in an open format that makes the data more accessible and improves performance and parallelization in reads and writes. Both approaches have been implemented successfully in the bioimaging community but both have limitations. At cloud-scale, those shortcomings limit scientific analysis and the sharing of results. We introduce here next-generation file formats (NGFF) as a solution to these challenges.