Legal Challenges and Market Rewards to the Use and Acceptance of Remote Sensing and Digital Information as Evidence

Bakgrund I den nutida forskningen är det essentiellt att företag tar hänsyn till medarbetarnas motivation så att de gynnas av det arbetssätt som tillämpas. En arbetsmetod som blivit allt vanligare är konceptet Lean som ursprungligen kommer från den japanska bilindustrin. Lean har idag utvecklats till ett allmängiltigt koncept som tillämpas i flertalet branscher världen över. Trots att konceptet innebär flertalet positiva aspekter har det fått utstå stark kritik när det kommer till de mänskliga aspekterna och forskare har ställt sig frågan om Lean är "Mean". Kritiken härleds främst till medarbetares arbetsmiljö i form av stress och brist på variation, självbestämmande, hälsa och välmående. Få empiriska studier har däremot genomförts som undersöker konsekvenserna som Lean får på medarbetares upplevda motivation. Syfte Vårt syfte är att undersöka och öka förståelsen för medarbetares upplevelser av motivationen i företag som tillämpar Lean. Vidare har studien för avsikt att utreda om det föreligger en paradox mellan Lean och vad som motiverar medarbetare på en arbetsplats. Metod Studien har utgått från en kvalitativ metod via intervjuer. För att göra en djupare undersökning och analysera hur vårt fenomen, motivation, upplevs i en kontext med Lean tillämpade vi Små-N-studier. Vi har även haft en iterativ forskningsansats som förenat den deduktiva och induktiva ansatsen där studien pendlat mellan teorier och empiriska observationer fram tills det slutgiltiga resultatet. Slutsatser Utefter medarbetarnas upplevelser har vi identifierat att det inte föreligger någon paradox mellan Lean och motivation eftersom övervägande antal medarbetare upplevde att de är motiverade även om företaget tillämpar Lean. Dock har studien kunnat urskilja både stödjande och motverkande faktorer när det kommer till medarbetarnas upplevda arbetsförhållanden som i sin tur inverkar på motivationen. De motverkande faktorerna menar vi främst beror på att arbetsförhållandena i somliga fall innehåller höga prestationskrav, målstyrning samt standardiseringar. Vidare upplevs motivationen överlag som mer positiv när företagen använder en mjukare form av Lean där samtliga medlemmars intressen beaktas.Background In modern research, it is essential that companies consider employees’ motivation so that they benefit from the applied practices. A working method that has become increasingly common is the concept Lean, which has its origin in the Japanese automotive industry. Today, Lean has evolved into a universal concept that is applied in many industries worldwide. Although the concept involves numerous positive aspects it has endured strong criticism when it comes to the human aspects and researchers have raised the question if Lean is "Mean". Criticism is derived primarily to employees’ working conditions in terms of stress and lack, variation, autonomy, health and wellbeing. However, few empirical studies have been carried out that examines the impact that Lean has on employees’ experienced motivation. Aim The aim is to increase the understanding of employees’ experienced motivation in companies that practice Lean. Further on the study has the intention to investigate if there is a paradox between Lean and what motivates employees on work. Methodology The study has been conducted through a qualitative method by interviews and to be able to do a deeper examination and analyze how our phenomenon, motivation, is experienced in a Lean context we applied small-N-studies. Our strategy has been iterative, combining both a deductive and inductive approach, where the study has varied between theories and empirical observations until the final result. Conclusions We have identified that there is no paradox between Lean and motivation since the majority of employees’ experienced that they are motivated even though the company practice Lean. Nevertheless the study shows that there are both supportive and counteractive factors when it comes to the employees’ experienced working conditions. The counteractive factors consists foremost of high performance standards, goal steering and standardizations, and have in some cases a negative influence on the working conditions. Furthermore the experienced motivation is more positive overall when the companies use a softer form of Lean where all the members’ interests are taken into account

Knowledge Management Practice at a Bulgarian Bank: A Case Study

This paper reports on knowledge management (KM) practices in the customer service and lending departments of one of Bulgaria's top retail banks and investigates how KM processes can be further improved. The Bank's KM activities have been studied using observations, interviews and informal discussions for data collection. Findings were compared and contrasted with existing literature in similar contexts. Although rudiments of knowledge sharing are evident from the KM activities in different departments of the bank, the limitations such as resistance to change of the implemented KM systems are impeding the effectiveness of the knowledge management process. More training and incentives are needed to increase knowledge creation and sharing. Moreover, a clearly articulated KM strategy along with success criteria and commitment and support from senior management is needed. There is a severe lack of knowledge management studies in Bulgarian context in general and Bulgarian banking sector in particular. The authors' findings will potentially help in improving knowledge sharing practice as well as provide a valuable insight into knowledge management related issues in the Bulgarian context. The findings from this research can be useful to companies from Eastern Europe and other regions in improving their knowledge sharing practice

Citizen Science 2.0 : Data Management Principles to Harness the Power of the Crowd

Citizen science refers to voluntary participation by the general public in scientific endeavors. Although citizen science has a long tradition, the rise of online communities and user-generated web content has the potential to greatly expand its scope and contributions. Citizens spread across a large area will collect more information than an individual researcher can. Because citizen scientists tend to make observations about areas they know well, data are likely to be very detailed. Although the potential for engaging citizen scientists is extensive, there are challenges as well. In this paper we consider one such challenge – creating an environment in which non-experts in a scientific domain can provide appropriate and accurate data regarding their observations. We describe the problem in the context of a research project that includes the development of a website to collect citizen-generated data on the distribution of plants and animals in a geographic region. We propose an approach that can improve the quantity and quality of data collected in such projects by organizing data using instance-based data structures. Potential implications of this approach are discussed and plans for future research to validate the design are described

Image mining: trends and developments

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining

Image mining: issues, frameworks and techniques

[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in significantly large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. Despite the development of many applications and algorithms in the individual research fields cited above, research in image mining is still in its infancy. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining at the end of this paper

Knowledge representation by connection matrices: A method for the on-board implementation of large expert systems

Extremely large knowledge sources and efficient knowledge access characterizing future real-life artificial intelligence applications represent crucial requirements for on-board artificial intelligence systems due to obvious computer time and storage constraints on spacecraft. A type of knowledge representation and corresponding reasoning mechanism is proposed which is particularly suited for the efficient processing of such large knowledge bases in expert systems

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

A Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to Knowledge Acquisition

Fuzzy Association Rule Mining Expert-Driven (FARME-D) approach to knowledge acquisition is proposed in this paper as a viable solution to the challenges of rule-based unwieldiness and sharp boundary problem in building a fuzzy rule-based expert system. The fuzzy models were based on domain experts’ opinion about the data description. The proposed approach is committed to modelling of a compact Fuzzy Rule-Based Expert Systems. It is also aimed at providing a platform for instant update of the knowledge-base in case new knowledge is discovered. The insight to the new approach strategies and underlining assumptions, the structure of FARME-D and its practical application in medical domain was discussed. Also, the modalities for the validation of the FARME-D approach were discussed