| 1. |
- Svensson, Ann, 1962-, et al.
(författare)
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Risk Factors When Implementing ERP Systems in Small Companies
- 2021
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Ingår i: Information. - : MDPI. - 2078-2489. ; 12:11, s. 1-11
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Tidskriftsartikel (refereegranskat)abstract
- Implementation of enterprise resource planning (ERP) systems often aims to improve the companies' processes in order to gain competitive advantage on the market. Especially, small companies need to integrate systems with suppliers and customers; hence, ERP systems often become a requirement. ERP system implementation processes in small enterprises contain several risk factors. Research has concluded that ERP implementation projects fail to a relatively high degree. Small companies are found to be constrained by limited resources, limited IS (information systems) knowledge and lack of IT expertise in ERP implementation. There are relatively few empirical research studies on implementing ERP systems in small enterprises and there is a large gap in research that could guide managers of small companies. This paper is based on a case study of three small enterprises that are planning to implement ERP systems that support their business processes. The aim of the paper is to identify the risk factors that can arise when implementing ERP systems in small enterprises. The analysis shows that an ERP system is a good solution to avoid using many different, separate systems in parallel. However, the study shows that it is challenging to integrate all systems used by suppliers and customers. An ERP system can include all information in one system and all information can also easily be accessed within that system. However, the implementation could be a demanding process as it requires engagement from all involved people, especially the managers of the companies.
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| 2. |
- Aggestam, Lena, et al.
(författare)
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Critical Success Factors in Capturing Knowledge for Retention in IT-Supported Repositories
- 2014
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Ingår i: Information. - : MDPI AG. - 2078-2489. ; 5:4, s. 558-569
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, the authors demonstrate the suitability of IT-supported knowledge repositories for knowledge retention. Successful knowledge retention is dependent on whatis stored in a repository and, hence, possible to share. Accordingly, the ability to capture theright (relevant) knowledge is a key aspect. Therefore, to increase the quality in an IT-supported knowledge repository, the identification activity, which starts the capture process, must besuccessfully performed. While critical success factors (CSFs) for knowledge retention andknowledge management are frequently discussed in the literature, there is a knowledge gapconcerning CSFs for this specific knowledge capture activity. From a knowledge retention perspective, this paper proposes a model that characterizes CSFs for the identification activity and highlights the CSFs’ contribution to knowledge retention.
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| 3. |
- Bartlett, Madeleine E., et al.
(författare)
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Requirements for Robotic Interpretation of Social Signals “in the Wild” : Insights from Diagnostic Criteria of Autism Spectrum Disorder
- 2020
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Ingår i: Information. - : MDPI. - 2078-2489. ; 11:2
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Tidskriftsartikel (refereegranskat)abstract
- The last few decades have seen widespread advances in technological means to characterise observable aspects of human behaviour such as gaze or posture. Among others, these developments have also led to significant advances in social robotics. At the same time, however, social robots are still largely evaluated in idealised or laboratory conditions, and it remains unclear whether the technological progress is sufficient to let such robots move “into the wild”. In this paper, we characterise the problems that a social robot in the real world may face, and review the technological state of the art in terms of addressing these. We do this by considering what it would entail to automate the diagnosis of Autism Spectrum Disorder (ASD). Just as for social robotics, ASD diagnosis fundamentally requires the ability to characterise human behaviour from observable aspects. However, therapists provide clear criteria regarding what to look for. As such, ASD diagnosis is a situation that is both relevant to real-world social robotics and comes with clear metrics. Overall, we demonstrate that even with relatively clear therapist-provided criteria and current technological progress, the need to interpret covert behaviour cannot yet be fully addressed. Our discussions have clear implications for ASD diagnosis, but also for social robotics more generally. For ASD diagnosis, we provide a classification of criteria based on whether or not they depend on covert information and highlight present-day possibilities for supporting therapists in diagnosis through technological means. For social robotics, we highlight the fundamental role of covert behaviour, show that the current state-of-the-art is unable to charact
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| 4. |
- Bashitialshaer, Raed, et al.
(författare)
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Obstacles to Applying Electronic Exams amidst the COVID-19 Pandemic : An Exploratory Study in the Palestinian Universities in Gaza
- 2021
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Ingår i: Information. - : MDPI. - 2078-2489. ; 12:6
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Tidskriftsartikel (refereegranskat)abstract
- In the context of the COVID-19 pandemic, we aim to identify and understand the obstacles and barriers in applying electronic exams successfully in the process of distance education. We followed an exploratory descriptive approach through a questionnaire (one general, open question) with a sample of university teachers and students in four of the largest universities Palestinian in Gaza. A total of 152 were returned from 300 distributed questionnaires. The results indicate that the university teachers and students faced 13 obstacles, of which 9 were shown to be shared between teachers and students, with a significant agreement in the regression analysis. Several of the obstacles perceived by respondents are in line with the literature and can be addressed by improved examination design, training, and preparation, or use of suitable software. Other obstacles related to infrastructure issues, leading to frequent power outages and unreliable internet access. Difficult living conditions in students’ homes and disparities in access to suitable devices or the internet make social equity in connection with high-stakes examinations a major concern. Some recommendations and suggestions are listed at the end of this study, considering local conditions in the Gaza governorates.
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| 5. |
- Burgin, Mark, et al.
(författare)
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Prolegomena to an operator theory of computation
- 2020
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Ingår i: Information (Switzerland). - : MDPI AG. - 2078-2489. ; 11:11
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Tidskriftsartikel (refereegranskat)abstract
- Defining computation as information processing (information dynamics) with information as a relational property of data structures (the difference in one system that makes a difference in another system) makes it very suitable to use operator formulation, with similarities to category theory. The concept of the operator is exceedingly important in many knowledge areas as a tool of theoretical studies and practical applications. Here we introduce the operator theory of computing, opening new opportunities for the exploration of computing devices, processes, and their networks.
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| 6. |
- Dodig-Crnkovic, Gordana
(författare)
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Dynamics of Information as Natural Computation
- 2011
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Ingår i: Information. - : MDPI AG. - 2078-2489. ; 2:3, s. 460-477
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Tidskriftsartikel (refereegranskat)abstract
- Processes considered rendering information dynamics have been studied, among others in: questions and answers, observations, communication, learning, belief revision, logical inference, game-theoretic interactions and computation. This article will put the computational approaches into a broader context of natural computation, where information dynamics is not only found in human communication and computational machinery but also in the entire nature. Information is understood as representing the world (reality as an informational web) for a cognizing agent, while information dynamics (information processing, computation) realizes physical laws through which all the changes of informational structures unfold. Computation as it appears in the natural world is more general than the human process of calculation modeled by the Turing machine. Natural computing is epitomized through the interactions of concurrent, in general asynchronous computational processes which are adequately represented by what Abramsky names “the second generation models of computation” which we argue to be the most general representation of information dynamics.
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| 7. |
- Dodig Crnkovic, Gordana, 1955, et al.
(författare)
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Floridi’s “Open Problems in Philosophy of Information”, Ten Years Later
- 2011
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Ingår i: Information. - : MDPI AG. - 2078-2489. ; 2:2, s. 327-359
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Tidskriftsartikel (refereegranskat)abstract
- In his article Open Problems in the Philosophy of Information 1 Luciano Floridi presented a Philosophy of Information research program in the form of eighteen open problems, covering the following fundamental areas: Information definition, information semantics, intelligence/cognition, informational universe/nature and values/ethics. We revisit Floridis program, highlighting some of the major advances, commenting on unsolved problems and rendering the new landscape of the Philosophy of Information (PI) emerging at present. As we analyze the progress of PI we try to situate Floridis program in the context of scientific and technological development that have been made last ten years. We emphasize that Philosophy of Information is a huge and vibrant research field, with its origins dating before Open Problems, and its domains extending even outside their scope. In this paper, we have been able only to sketch some of the developments during the past ten years. Our hope is that, even if fragmentary, this review may serve as a contribution to the effort of understanding the present state of the art and the paths of development of Philosophy of Information as seen through the lens of Open Problems.
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| 8. |
- Dodig Crnkovic, Gordana, 1955
(författare)
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Physical computation as dynamics of form that glues everything together
- 2012
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Ingår i: Information (Switzerland). - : MDPI AG. - 2078-2489. ; 3:2, s. 204-218
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Tidskriftsartikel (refereegranskat)abstract
- The framework is proposed where matter can be seen as related to energy in a way structure relates to process and information relates to computation. In this scheme matter corresponds to a structure, which corresponds to information. Energy corresponds to the ability to carry out a process, which corresponds to computation. The relationship between each two complementary parts of each dichotomous pair (matter/energy, structure/process, information/computation) are analogous to the relationship between being and becoming, where being is the persistence of an existing structure while becoming is the emergence of a new structure through the process of interactions. This approach presents a unified view built on two fundamental ontological categories: Information and computation. Conceptualizing the physical world as an intricate tapestry of protoinformation networks evolving through processes of natural computation helps to make more coherent models of nature, connecting non-living and living worlds. It presents a suitable basis for incorporating current developments in understanding of biological/cognitive/social systems as generated by complexification of physicochemical processes through self-organization of molecules into dynamic adaptive complex systems by morphogenesis, adaptation and learning-all of which are understood as information processing.
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| 9. |
- Durst, Susanne, et al.
(författare)
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Knowledge Leakages and Ways to Reduce Them in Small and Medium-Sized Enterprises (SMEs)
- 2014
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Ingår i: Information. - Basel : M D P I AG. - 2078-2489. ; 5:3, s. 440-450
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, we look into knowledge leakages and ways to address them. It is conducted from the point of view of small and medium-sized enterprises (SMEs), as their specific attributes create unique challenges. Based on a discussion of the relevant fields, ways are presented in order to reduce the danger of knowledge leakages. In view of practitioners, the paper’s findings may enable an increased awareness towards the areas where existing knowledge is at the mercy of “leakage”. This can assist managers of SMEs to better cope with risks related to knowledge leakage and, therefore, better exploit the (limited) knowledge base available.
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| 10. |
- Guo, Hong, et al.
(författare)
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Ontology-Based Domain Analysis for Model Driven Pervasive Game Development
- 2018
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Ingår i: Information. - : MDPI. - 2078-2489. ; 9:5
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Tidskriftsartikel (refereegranskat)abstract
- Domain Analysis (DA) plays an important role in Model Driven Development (MDD) and Domain-Specific Modeling (DSM). However, most formal DA methods are heavy weight and not practical sometimes. For instance, when computer games are developed, the problem domain (game design) is decided gradually within numerous iterations. It is not practical to fit a heavy-weight DA in such an agile process. In this research, we propose a light-weight DA which can be embedded in the original game development process. The DA process is based on a game ontology which serves for both game design and domain analysis. In this paper, we introduce the ontology and demonstrate how to use it in the domain analysis process. We discuss the quality and evaluate the ontology with a user acceptance survey. The test result shows that most potential users considered the ontology useful and easy to use.
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| 11. |
- Jiang, Haiyang, et al.
(författare)
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Machine-Learning-Based User Position Prediction and Behavior Analysis for Location Services
- 2021
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Ingår i: Information. - : MDPI AG. - 2078-2489. ; 12:5
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Tidskriftsartikel (refereegranskat)abstract
- Machine learning (ML)-based methods are increasingly used in different fields of business to improve the quality and efficiency of services. The increasing amount of data and the development of artificial intelligence algorithms have improved the services provided to customers in shopping malls. Most new services are based on customers' precise positioning in shopping malls, especially customer positioning within shops. We propose a novel method to accurately predict the specific shops in which customers are located in shopping malls. We use global positioning system (GPS) information provided by customers' mobile terminals and WiFi information that completely covers the shopping mall. According to the prediction results, we learn some of the behavior preferences of users. We use these predicted customer locations to provide customers with more accurate services. Our training dataset is built using feature extraction and screening from some real customers' transaction records in shopping malls. In order to prove the validity of the model, we also cross-check our algorithm with a variety of machine learning algorithms. Our method achieves the best speed-accuracy trade-off and can accurately locate the shops in which customers are located in shopping malls in real time. Compared to other algorithms, the proposed model is more accurate. User preference behaviors can be used in applications to efficiently provide more tailored services.
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| 12. |
- Johansson, Mikael, 1987, et al.
(författare)
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The decline of user experience in transition from automated driving to manual driving
- 2021
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Ingår i: Information (Switzerland). - Basel : MDPI AG. - 2078-2489. ; 12:3
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Tidskriftsartikel (refereegranskat)abstract
- Automated driving technologies are rapidly being developed. However, until vehicles are fully automated, the control of the dynamic driving task will be shifted between the driver and automated driving system. This paper aims to explore how transitions from automated driving to manual driving affect user experience and how that experience correlates to take-over performance. In the study 20 participants experienced using an automated driving system during rush-hour traffic in the San Francisco Bay Area, CA, USA. The automated driving system was available in congested traffic situations and when active, the participants could engage in non-driving related activities. The participants were interviewed afterwards regarding their experience of the transitions. The findings show that most of the participants experienced the transition from automated driving to manual driving as negative. Their user experience seems to be shaped by several reasons that differ in temporality and are derived from different phases during the transition process. The results regarding correlation between participants’ experience and take-over performance are inconclusive, but some trends were identified. The study highlights the need for new design solutions that do not only improve drivers’ take-over performance, but also enhance user experience during take-over requests from automated to manual driving.
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| 13. |
- Kampik, Timotheus, 1989-, et al.
(författare)
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Simulating, Off-Chain and On-Chain : Agent-Based Simulations in Cross-Organizational Business Processes
- 2020
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Ingår i: Information. - Basel : MDPI. - 2078-2489. ; 11:1
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Tidskriftsartikel (refereegranskat)abstract
- Information systems execute increasingly complex business processes, often across organizations. Blockchain technology has emerged as a potential facilitator of (semi)-autonomous cross-organizational business process execution; in particular, so-called consortium blockchains can be considered as promising enablers in this context, as they do not require the use of cryptocurrency-based blockchain technology, as long as the trusted (authenticated) members of the network are willing to provide computing resources for consensus-finding. However, increased autonomy in the execution of business processes also requires the delegation of business decisions to machines. To support complex decision-making processes by assessing potential future outcomes, agent-based simulations can be considered a useful tool for the autonomous enterprise. In this paper, we explore the intersection of multi-agent simulations and consortium blockchain technology in the context of enterprise applications by devising architectures and technology stacks for both off-chain and on-chain agent-based simulation in the context of blockchain-based business process execution.
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| 14. |
- Khoshkangini, Reza, 1984-, et al.
(författare)
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Early Prediction of Quality Issues in Automotive Modern Industry
- 2020
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Ingår i: Information. - Basel : MDPI. - 2078-2489. ; 11:7
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Tidskriftsartikel (refereegranskat)abstract
- Many industries today are struggling with early identification of quality issues, given the shortening of product design cycles and the desire to decrease production costs, coupled with customers' requirement for high uptime. The vehicle industry is no exception, as breakdowns often lead to on-road stops and delays in delivery missions. In this paper we consider quality issues to be an unexpected increase in failure rates of a particular component; those are particularly problematic for the Original Equipment Manufacturers (OEMs) since they lead to unplanned costs and can significantly affect brand value. We propose a new approach towards the early detection of quality issues using Machine Learning (ML) to forecast the failures of a given component across the large population of units.In this study, we combine the usage information of vehicles with the records of their failures. The former is continuously collected, as the usage statistics are transmitted over telematics connections. The latter is based on invoice and warranty information collected in the workshops. We compare two different ML approaches: the first is an auto-regression model of the failure ratios for vehicles based on past information, while the second is the aggregation of individual vehicle failure predictions based on their individual usage.We present experimental evaluations on the real data captured from heavy-duty trucks demonstrating how these two formulations have complementary strengths and weaknesses; in particular, they can outperform each other given different volumes of the data. The classification approach surpasses the regressor model whenever enough data is available, i.e., once the vehicles are in-service for a longer time. On the other hand, the regression shows better predictive performance with a smaller amount of data, i.e., for vehicles that have been deployed recently. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
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| 15. |
- Kristoffersson, Annica, et al.
(författare)
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Wearable Sensors for Monitoring and Preventing Noncommunicable Diseases : A Systematic Review
- 2020
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Ingår i: Information. - : MDPI AG. - 2078-2489. ; 11:11, s. 1-31
-
Forskningsöversikt (refereegranskat)abstract
- Ensuring healthy lives and promoting a healthy well-being for all at all ages are listed as some of the goals in Agenda 2030 for Sustainable Development. Considering that noncommunicable diseases (NCDs) are the leading cause of death worldwide, reducing the mortality of NCDs is an important target. To reach this goal, means for detecting and reacting to warning signals are necessary. Here, remote health monitoring in real time has great potential. This article provides a systematic review of the use of wearable sensors for the monitoring and prevention of NCDs. In addition, this article not only provides in-depth information about the retrieved articles, but also discusses examples of studies assessing warning signals that may result in serious health conditions, such as stroke and cardiac arrest, if left untreated. One finding is that even though many good examples of wearable sensor systems for monitoring and controlling NCDs are presented, many issues also remain to be solved. One major issue is the lack of testing on representative people from a sociodemographic perspective. Even though substantial work remains, the use of wearable sensor systems has a great potential to be used in the battle against NCDs by providing the means to diagnose, monitor and prevent NCDs.
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| 16. |
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| 17. |
- Li, Yuhong, et al.
(författare)
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Context-aware data dissemination for ICN-based vehicular ad hoc networks
- 2018
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Ingår i: Information. - : MDPI AG. - 2078-2489. ; 9:11
-
Tidskriftsartikel (refereegranskat)abstract
- Information-centric networking (ICN) technology matches many major requirements of vehicular ad hoc networks (VANETs) in terms of its connectionless networking paradigm accordant with the dynamic environments of VANETs and is increasingly being applied to VANETs. However, wireless transmissions of packets in VANETs using ICN mechanisms can lead to broadcast storms and channel contention, severely affecting the performance of data dissemination. At the same time, frequent changes of topology due to driving at high speeds and environmental obstacles can also lead to link interruptions when too few vehicles are involved in data forwarding. Hence, balancing the number of forwarding vehicular nodes and the number of copies of packets that are forwarded is essential for improving the performance of data dissemination in information-centric networking for vehicular ad-hoc networks. In this paper, we propose a context-aware packet-forwarding mechanism for ICN-based VANETs. The relative geographical position of vehicles, the density and relative distribution of vehicles, and the priority of content are considered during the packet forwarding. Simulation results show that the proposed mechanism can improve the performance of data dissemination in ICN-based VANET in terms of a successful data delivery ratio, packet loss rate, bandwidth usage, data response time, and traversed hops.
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| 18. |
- Maskeliunas, Rytis, et al.
(författare)
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Serious Game iDO: Towards Better Education in Dementia Care
- 2019
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Ingår i: Information. - : MDPI AG. - 2078-2489. ; 10:11, s. 1-15
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Tidskriftsartikel (refereegranskat)abstract
- We describe the iDO serious game developed during implementation of the Innovative Digital Training Opportunities on Dementia for Direct Care Workers (IDO) project. The project targets formal and informal caregivers of persons with dementia in order to improve caregiver knowledge and competences skills with a non-traditional source of training. This paper describes the steps faced to define the iDO caregiver behavior improvement model, design of game mechanics, development of game art and game characters, and implementation of gameplay. Furthermore, it aimed to assess the direct impact of the game on caregivers (n = 48) and seniors with early signs of dementia (n = 14) in Lithuania measured with the Geriatric Depression Scale (GDS) and Dementia Attitudes Scale (DAS). The caregivers’ GDS scores showed a decrease in negative answers from 13.4% (pre-game survey) to 5.2% (post-game survey). The seniors’ GDS scores showed a decrease in negative answers from 24.9% (pre-game survey) to 10.9% (post-game survey). The overall DAS scores increased from 6.07 in the pre-game survey to 6.41 in the post-game survey, statistically significant for both caregivers and seniors (p < 0.001), respectively. We conclude that the game aroused positive moods and attitudes for future caregivers of persons with dementia, indicating a more relaxed status and a decreased fear in accomplishing the caring process.
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| 19. |
- Nygren, Thomas, 1972-, et al.
(författare)
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Combatting Visual Fake News with a Professional Fact-Checking Tool in Education in France, Romania, Spain and Sweden
- 2021
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Ingår i: Information. - : MDPI AG. - 2078-2489. ; 12:5
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Tidskriftsartikel (refereegranskat)abstract
- Educational and technical resources are regarded as central in combating disinformation and safeguarding democracy in an era of ‘fake news’. In this study, we investigated whether a professional fact-checking tool could be utilised in curricular activity to make pupils more skilled in determining the credibility of digital news and to inspire them to use digital tools to further their transliteracy and technocognition. In addition, we explored how pupils’ performance and attitudes regarding digital news and tools varied across four countries (France, Romania, Spain, and Sweden). Our findings showed that a two-hour intervention had a statistically significant impact on teenagers’ abilities to determine the credibility of fake images and videos. We also found that the intervention inspired pupils to use digital tools in information credibility assessments. Importantly, the intervention did not make pupils more sceptical of credible news. The impact of the intervention was greater in Romania and Spain than among pupils in Sweden and France. The greater impact in these two countries, we argue, is due to cultural context and the fact that pupils in Romania and Spain learned to focus less on ’gut feelings’, increased their use of digital tools, and had a more positive attitude toward the use of the fact-checking tool than pupils in Sweden and France.
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| 20. |
- Padyab, Ali Mohammad, et al.
(författare)
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Adoption Barriers of IoT in Large Scale Pilots
- 2020
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Ingår i: Information. - : MDPI. - 2078-2489. ; 11:23, s. 1-23
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Tidskriftsartikel (refereegranskat)abstract
- The pervasive connectivity of devices enabled by Internet of Things (IoT) technologies is leading the way in various innovative services and applications. This increasing connectivity comes with its own complexity. Thus, large scale pilots (LSPs) are designed to develop, test and use IoT innovations in various domains in conditions very similar to their operational scalable setting. One of the key challenges facing the diffusion of such innovations within the course of an LSP is understanding the conditions in which their respective users decide to adopt them (or not). Accordingly, in this study we explore IoT adoption barriers in four LSPs in Europe from the following domains: smart cities, autonomous driving, wearables and smart agriculture and farming. By applying Roger’s Diffusion of Innovation as a theoretical lens and using empirical data from workshops and expert interviews, we identify a set of common and domain specific adoption barriers. Our results reveal that trust, cost, perceived value, privacy and security are common concerns, yet shape differently across domains. In order to overcome various barriers, the relative advantage or value of using the innovation needs to be clearly communicated and related to the users’ situational use; while this value can be economic in some domains, it is more hedonic in others. LSPs were particularly challenged in applying established strategies to overcome some of those barriers (e.g., co-creation with end-users) due to the immaturity of the technology as well as the scale of pilots. Accordingly, we reflect on the theoretical choice in the discussion as well as the implications of this study on research and practice. We conclude with providing practical recommendations to LSPs and avenues for future research
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| 21. |
- Samini, Ali, 1979-, et al.
(författare)
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Wand-Like Interaction with a Hand-Held Tablet Device : A study on Selection and Pose Manipulation Techniques
- 2019
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Ingår i: Information. - : MDPI. - 2078-2489. ; 10:4
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Tidskriftsartikel (refereegranskat)abstract
- Current hand-held smart devices are supplied with powerful processors, high resolution screens, and sharp cameras that make them suitable for Augmented Reality (AR) applications. Such applications commonly use interaction techniques adapted for touch, such as touch selection and multi-touch pose manipulation, mapping 2D gestures to 3D action. To enable direct 3D interaction for hand-held AR, an alternative is to use the changes of the device pose for 6 degrees-of-freedom interaction. In this article we explore selection and pose manipulation techniques that aim to minimize the amount of touch. For this, we explore and study the characteristics of both non-touch selection and non-touch pose manipulation techniques. We present two studies that, on the one hand, compare selection techniques with the common touch selection and, on the other, investigate the effect of user gaze control on the non-touch pose manipulation techniques.
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| 22. |
- Telea, Alexandru, et al.
(författare)
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Special Issue on Selected Papers from IVAPP 2018
- 2018
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Ingår i: Information. - : MDPI. - 2078-2489. ; 9:7
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Recent developments at the crossroads of data science, datamining,machine learning, and graphics and imaging sciences have further established information visualization and visual analytics as central disciplines that deliver methods, techniques, and tools for making sense of and extracting actionable insights and results fromlarge amounts of complex,multidimensional, hybrid, and time-dependent data.[...]
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| 23. |
- Verdiesen, Ilse, et al.
(författare)
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Integrating comprehensive human oversight in drone deployment : A conceptual framework applied to the case of military surveillance drones
- 2021
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Ingår i: Information. - : MDPI. - 2078-2489. ; 12:9
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Tidskriftsartikel (refereegranskat)abstract
- Accountability is a value often mentioned in the debate on intelligent systems and their increased pervasiveness in our society. When focusing specifically on autonomous systems, a critical gap emerges: although there is much work on governance and attribution of accountability, there is a significant lack of methods for the operationalisation of accountability within the socio-technical layer of autonomous systems. In the case of autonomous unmanned aerial vehicles-or drones— the critical question of how to maintain accountability as they undertake fully autonomous flights becomes increasingly important as their uses multiply in both the commercial and military fields. In this paper, we aim to fill the operationalisation gap by proposing a socio-technical framework to guarantee human oversight and accountability in drone deployments, showing its enforceability in the real case of military surveillance drones. By keeping a focus on accountability and human oversight as values, we align with the emphasis placed on human responsibility, while requiring a concretisation of what these principles mean for each specific application, connecting them with concrete socio-technical requirements. In addition, by constraining the framework to observable elements of pre-and post-deployment, we do not rely on assumptions made on the internal workings of the drone nor the technical fluency of the operator.
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| 24. |
- Vogel, Bahtijar, et al.
(författare)
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Openness and Security Thinking Characteristics for IoT Ecosystems
- 2020
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Ingår i: Information. - Basel, Switzerland : MDPI. - 2078-2489. ; 11:12
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Tidskriftsartikel (refereegranskat)abstract
- While security is often recognized as a top priority for organizations and a push for competitive advantage, repeatedly, Internet of Things (IoT) products have become a target of diverse security attacks. Thus, orchestrating smart services and devices in a more open, standardized and secure way in IoT environments is yet a desire as much as it is a challenge. In this paper, we propose a model for IoT practitioners and researchers, who can adopt a sound security thinking in parallel with open IoT technological developments. We present the state-of-the-art and an empirical study with IoT practitioners. These efforts have resulted in identifying a set of openness and security thinking criteria that are important to consider from an IoT ecosystem point of view. Openness in terms of open standards, data, APIs, processes, open source and open architectures (flexibility, customizability and extensibility aspects), by presenting security thinking tackled from a three-dimensional point of view (awareness, assessment and challenges) that highlight the need to develop an IoT security mindset. A novel model is conceptualized with those characteristics followed by several key aspects important to design and secure future IoT systems.
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| 25. |
- Wu, Peng, et al.
(författare)
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Reasoning Method between Polynomial Error Assertions
- 2021
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Ingår i: Information. - : MDPI. - 2078-2489. ; 12:8
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Tidskriftsartikel (refereegranskat)abstract
- Error coefficients are ubiquitous in systems. In particular, errors in reasoning verification must be considered regarding safety-critical systems. We present a reasoning method that can be applied to systems described by the polynomial error assertion (PEA). The implication relationship between PEAs can be converted to an inclusion relationship between zero sets of PEAs; the PEAs are then transformed into first-order polynomial logic. Combined with the quantifier elimination method, based on cylindrical algebraic decomposition, the judgment of the inclusion relationship between zero sets of PEAs is transformed into judgment error parameters and specific error coefficient constraints, which can be obtained by the quantifier elimination method. The proposed reasoning method is validated by proving the related theorems. An example of intercepting target objects is provided, and the correctness of our method is tested through large-scale random cases. Compared with reasoning methods without error semantics, our reasoning method has the advantage of being able to deal with error parameters.
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| 26. |
- Adewumi, Tosin, 1978-, et al.
(författare)
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State-of-the-Art in Open-Domain Conversational AI: A Survey
- 2022
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Ingår i: Information. - : MDPI. - 2078-2489. ; 13:6
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Forskningsöversikt (refereegranskat)abstract
- We survey SoTA open-domain conversational AI models with the objective of presenting the prevailing challenges that still exist to spur future research. In addition, we provide statistics on the gender of conversational AI in order to guide the ethics discussion surrounding the issue. Open-domain conversational AI models are known to have several challenges, including bland, repetitive responses and performance degradation when prompted with figurative language, among others. First, we provide some background by discussing some topics of interest in conversational AI. We then discuss the method applied to the two investigations carried out that make up this study. The first investigation involves a search for recent SoTA open-domain conversational AI models, while the second involves the search for 100 conversational AI to assess their gender. Results of the survey show that progress has been made with recent SoTA conversational AI, but there are still persistent challenges that need to be solved, and the female gender is more common than the male for conversational AI. One main takeaway is that hybrid models of conversational AI offer more advantages than any single architecture. The key contributions of this survey are (1) the identification of prevailing challenges in SoTA open-domain conversational AI, (2) the rarely held discussion on open-domain conversational AI for low-resource languages, and (3) the discussion about the ethics surrounding the gender of conversational AI.
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| 27. |
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| 28. |
- Arshed, Muhammad Asad, et al.
(författare)
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Chem2Side : A Deep Learning Model with Ensemble Augmentation (Conventional + Pix2Pix) for COVID-19 Drug Side-Effects Prediction from Chemical Images
- 2023
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Ingår i: Information (Switzerland). - 2078-2489. ; 14:12
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Tidskriftsartikel (refereegranskat)abstract
- Drug side effects (DSEs) or adverse drug reactions (ADRs) are a major concern in the healthcare industry, accounting for a significant number of annual deaths in Europe alone. Identifying and predicting DSEs early in the drug development process is crucial to mitigate their impact on public health and reduce the time and costs associated with drug development. Objective: In this study, our primary objective is to predict multiple drug side effects using 2D chemical structures, especially for COVID-19, departing from the conventional approach of relying on 1D chemical structures. We aim to develop a novel model for DSE prediction that leverages the CNN-based transfer learning architecture of ResNet152V2. Motivation: The motivation behind this research stems from the need to enhance the efficiency and accuracy of DSE prediction, enabling the pharmaceutical industry to identify potential drug candidates with fewer adverse effects. By utilizing 2D chemical structures and employing data augmentation techniques, we seek to revolutionize the field of drug side-effect prediction. Novelty: This study introduces several novel aspects. The proposed study is the first of its kind to use 2D chemical structures for predicting drug side effects, departing from the conventional 1D approaches. Secondly, we employ data augmentation with both conventional and diffusion-based models (Pix2Pix), a unique strategy in the field. These innovations set the stage for a more advanced and accurate approach to DSE prediction. Results: Our proposed model, named CHEM2SIDE, achieved an impressive average training accuracy of 0.78. Moreover, the average validation and test accuracy, precision, and recall were all at 0.73. When evaluated for COVID-19 drugs, our model exhibited an accuracy of 0.72, a precision of 0.79, a recall of 0.72, and an F1 score of 0.73. Comparative assessments against established transfer learning and machine learning models (VGG16, MobileNetV2, DenseNet121, and KNN) showcased the exceptional performance of CHEM2SIDE, marking a significant advancement in drug side-effect prediction. Conclusions: Our study introduces a groundbreaking approach to predicting drug side effects by using 2D chemical structures and incorporating data augmentation. The CHEM2SIDE model demonstrates remarkable accuracy and outperforms existing models, offering a promising solution to the challenges posed by DSEs in drug development. This research holds great potential for improving drug safety and reducing the associated time and costs.
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| 29. |
- Dodig Crnkovic, Gordana, 1955
(författare)
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Information and energy/matter
- 2012
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Ingår i: Information (Switzerland). - : MDPI AG. - 2078-2489. ; 3:4, s. 751-755
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Tidskriftsartikel (refereegranskat)abstract
- 1. The Necessity of an Agent (Observer/Actor) for Knowledge Generation2. Reality (for an Agent) Is an Informational Structure3. Information is both Discrete and Continuous4. It from (Qu)bit5. Self-Organized Complexity, Jaynesian Observers and Logic in Reality
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| 30. |
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| 31. |
- Gill, Mehwish, et al.
(författare)
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A Novel Predictor for the Analysis and Prediction of Enhancers and Their Strength via Multi-View Features and Deep Forest
- 2023
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Ingår i: Information (Switzerland). - 2078-2489. ; 14:12
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Tidskriftsartikel (refereegranskat)abstract
- Enhancers are short DNA segments (50–1500 bp) that effectively activate gene transcription when transcription factors (TFs) are present. There is a correlation between the genetic differences in enhancers and numerous human disorders including cancer and inflammatory bowel disease. In computational biology, the accurate categorization of enhancers can yield important information for drug discovery and development. High-throughput experimental approaches are thought to be vital tools for researching enhancers’ key characteristics; however, because these techniques require a lot of labor and time, it might be difficult for researchers to forecast enhancers and their powers. Therefore, computational techniques are considered an alternate strategy for handling this issue. Based on the types of algorithms that have been used to construct predictors, the current methodologies can be divided into three primary categories: ensemble-based methods, deep learning-based approaches, and traditional ML-based techniques. In this study, we developed a novel two-layer deep forest-based predictor for accurate enhancer and strength prediction, namely, NEPERS. Enhancers and non-enhancers are divided at the first level by NEPERS, whereas strong and weak enhancers are divided at the second level. To evaluate the effectiveness of feature fusion, block-wise deep forest and other algorithms were combined with multi-view features such as PSTNPss, PSTNPdss, CKSNAP, and NCP via 10-fold cross-validation and independent testing. Our proposed technique performs better than competing models across all parameters, with an ACC of 0.876, Sen of 0.864, Spe of 0.888, MCC of 0.753, and AUC of 0.940 for layer 1 and an ACC of 0.959, Sen of 0.960, Spe of 0.958, MCC of 0.918, and AUC of 0.990 for layer 2, respectively, for the benchmark dataset. Similarly, for the independent test, the ACC, Sen, Spe, MCC, and AUC were 0.863, 0.865, 0.860, 0.725, and 0.948 for layer 1 and 0.890, 0.940, 0.840, 0.784, and 0.951 for layer 2, respectively. This study provides conclusive insights for the accurate and effective detection and characterization of enhancers and their strengths.
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| 32. |
- Maskeliunas, Rytis, et al.
(författare)
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Deep Reinforcement Learning-Based iTrain Serious Game for Caregivers Dealing with Post-Stroke Patients
- 2022
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Ingår i: Information. - : MDPI. - 2078-2489. ; 13:12
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Tidskriftsartikel (refereegranskat)abstract
- This paper describes a serious game based on a knowledge transfer model using deep reinforcement learning, with an aim to improve the caretakers knowledge and abilities in post-stroke care. The iTrain game was designed to improve caregiver knowledge and abilities by providing non-traditional training to formal and informal caregivers who deal with stroke survivors. The methodologies utilized professional medical experiences and real-life evidence data gathered during the duration of the iTrain project to create the scenarios for the games deep reinforcement caregiver behavior improvement model, as well as the design of game mechanics, game images and game characters, and gameplay implementation. Furthermore, the results of the games direct impact on caregivers (n = 25) and stroke survivors (n = 21) in Lithuania using the Geriatric Depression Scale (GDS) and user experience questionnaire (UEQ) are presented. Both surveys had favorable outcomes, showing the effectiveness of the approach. The GDS scale (score 10) revealed a low number of 28% of individuals depressed, and the UEQ received a very favorable grade of +0.8.
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| 33. |
- Munir, Hussan, Assistant Professor, et al.
(författare)
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Artificial Intelligence and Machine Learning Approaches in Digital Education : A Systematic Revision
- 2022
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Ingår i: Information. - : MDPI. - 2078-2489. ; 13:4
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Forskningsöversikt (refereegranskat)abstract
- The use of artificial intelligence and machine learning techniques across all disciplines has exploded in the past few years, with the ever-growing size of data and the changing needs of higher education, such as digital education. Similarly, online educational information systems have a huge amount of data related to students in digital education. This educational data can be used with artificial intelligence and machine learning techniques to improve digital education. This study makes two main contributions. First, the study follows a repeatable and objective process of exploring the literature. Second, the study outlines and explains the literature's themes related to the use of AI-based algorithms in digital education. The study findings present six themes related to the use of machines in digital education. The synthesized evidence in this study suggests that machine learning and deep learning algorithms are used in several themes of digital learning. These themes include using intelligent tutors, dropout predictions, performance predictions, adaptive and predictive learning and learning styles, analytics and group-based learning, and automation. artificial neural network and support vector machine algorithms appear to be utilized among all the identified themes, followed by random forest, decision tree, naive Bayes, and logistic regression algorithms.
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| 34. |
- Nikmanesh, M., et al.
(författare)
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Employee Productivity Assessment Using Fuzzy Inference System
- 2023
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Ingår i: Information. - 2078-2489. ; 14:7
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Tidskriftsartikel (refereegranskat)abstract
- The success of an organization hinges upon the effective utilization of its human resources, which serves as a crucial developmental factor and competitive advantage, and sets the organization apart from others. Evaluating staff productivity involves considering various dimensions, notably structural, behavioral, and circumferential factors. These factors collectively form a three-pronged model that comprehensively encompasses the facets of an organization. However, assessing the productivity of employees poses challenges, due to the inherent complexity of the humanities domain. Fuzzy logic offers a sound approach to address this issue, employing its rationale and leveraging a fuzzy inference system (FIS) as a sophisticated toolbox for measuring productivity. Fuzzy inference systems enhance the flexibility, speed, and adaptability in soft computation. Likewise, their applications, integration, hybridization, and adaptation are also introduced. They also provide an alternative solution to deal with imprecise data. In this study, we endeavored to identify and measure the productivity of human resources within a case study, by developing an alternative framework known as an FIS. Our findings provided evidence to support the validity of the alternative approach. Thus, the utilized approach for assessing employee productivity may provide managers and businesses with a more realistic asset.
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| 35. |
- Rezk, Nesma M., 1987-, et al.
(författare)
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Shrink and Eliminate : A Study of Post-Training Quantization and Repeated Operations Elimination in RNN Models
- 2022
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Ingår i: Information. - Basel : MDPI. - 2078-2489. ; 13:4
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Tidskriftsartikel (refereegranskat)abstract
- Recurrent neural networks (RNNs) are neural networks (NN) designed for time-series applications. There is a growing interest in running RNNs to support these applications on edge devices. However, RNNs have large memory and computational demands that make them challenging to implement on edge devices. Quantization is used to shrink the size and the computational needs of such models by decreasing weights and activation precision. Further, the delta networks method increases the sparsity in activation vectors by relying on the temporal relationship between successive input sequences to eliminate repeated computations and memory accesses. In this paper, we study the effect of quantization on LSTM-, GRU-, LiGRU-, and SRU-based RNN models for speech recognition on the TIMIT dataset. We show how to apply post-training quantization on these models with a minimal increase in the error by skipping quantization of selected paths. In addition, we show that the quantization of activation vectors in RNNs to integer precision leads to considerable sparsity if the delta networks method is applied. Then, we propose a method for increasing the sparsity in the activation vectors while minimizing the error and maximizing the percentage of eliminated computations. The proposed quantization method managed to com-press the four models more than 85%, with an error increase of 0.6, 0, 2.1, and 0.2 percentage points, respectively. By applying the delta networks method to the quantized models, more than 50% of the operations can be eliminated, in most cases with only a minor increase in the error. Comparing the four models to each other under the quantization and delta networks method, we found that compressed LSTM-based models are the most-optimum solutions at low-error-rates constraints. The compressed SRU-based models are the smallest in size, suitable when higher error rates are acceptable, and the compressed LiGRU-based models have the highest number of eliminated operations.
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| 36. |
- Rouchitsas, Alexandros, 1980-, et al.
(författare)
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Ghost on the Windshield: Employing a Virtual Human Character to Communicate Pedestrian Acknowledgement and Vehicle Intention
- 2022
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Ingår i: Information. - : MDPI. - 2078-2489. ; 13:9
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Tidskriftsartikel (refereegranskat)abstract
- Pedestrians base their street-crossing decisions on vehicle-centric as well as driver-centric cues. In the future, however, drivers of autonomous vehicles will be preoccupied with non-driving related activities and will thus be unable to provide pedestrians with relevant communicative cues. External human–machine interfaces (eHMIs) hold promise for filling the expected communication gap by providing information about a vehicle’s situational awareness and intention. In this paper, we present an eHMI concept that employs a virtual human character (VHC) to communicate pedestrian acknowledgement and vehicle intention (non-yielding; cruising; yielding). Pedestrian acknowledgement is communicated via gaze direction while vehicle intention is communicated via facial expression. The effectiveness of the proposed anthropomorphic eHMI concept was evaluated in the context of a monitor-based laboratory experiment where the participants performed a crossing intention task (self-paced, two-alternative forced choice) and their accuracy in making appropriate street-crossing decisions was measured. In each trial, they were first presented with a 3D animated sequence of a VHC (male; female) that either looked directly at them or clearly to their right while producing either an emotional (smile; angry expression; surprised expression), a conversational (nod; head shake), or a neutral (neutral expression; cheek puff) facial expression. Then, the participants were asked to imagine they were pedestrians intending to cross a one-way street at a random uncontrolled location when they saw an autonomous vehicle equipped with the eHMI approaching from the right and indicate via mouse click whether they would cross the street in front of the oncoming vehicle or not. An implementation of the proposed concept where non-yielding intention is communicated via the VHC producing either an angry expression, a surprised expression, or a head shake; cruising intention is communicated via the VHC puffing its cheeks; and yielding intention is communicated via the VHC nodding, was shown to be highly effective in ensuring the safety of a single pedestrian or even two co-located pedestrians without compromising traffic flow in either case. The implications for the development of intuitive, culture-transcending eHMIs that can support multiple pedestrians in parallel are discussed.
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| 37. |
- Russmann, Uta, et al.
(författare)
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Studying Organizations on Instagram
- 2016
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Ingår i: Information. - : MDPI AG. - 2078-2489. ; 7:4
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Tidskriftsartikel (refereegranskat)abstract
- With the rise of social media platforms based on the sharing of pictures and videos, the question of how such platforms should be studied arises. Previous research on social media (content) has mainly focused on text (written words) and the rather text-based social media platforms Twitter and Facebook. Drawing on research in the fields of visual, political, and business communication, we introduce a methodological framework to study the fast-growing image-sharing service Instagram. This methodological framework was developed to study political parties' Instagram accounts and tested by means of a study of Swedish political parties during the 2014 election campaign. In this article, we adapt the framework to also study other types of organizations active on Instagram by focusing on the following main questions: Do organizations only use Instagram to share one-way information, focusing on disseminating information and self-presentation? Or is Instagram used for two-way communication to establish and cultivate organization-public relationships? We introduce and discuss the coding of variables with respect to four clusters: the perception of the posting, image management, integration, and interactivity.
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| 38. |
- Zhang, L., et al.
(författare)
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Improved Detection Method for Micro-Targets in Remote Sensing Images
- 2024
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Ingår i: Information. - : Multidisciplinary Digital Publishing Institute (MDPI). - 2078-2489. ; 15:2
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Tidskriftsartikel (refereegranskat)abstract
- With the exponential growth of remote sensing images in recent years, there has been a significant increase in demand for micro-target detection. Recently, effective detection methods for small targets have emerged; however, for micro-targets (even fewer pixels than small targets), most existing methods are not fully competent in feature extraction, target positioning, and rapid classification. This study proposes an enhanced detection method, especially for micro-targets, in which a combined loss function (consisting of NWD and CIOU) is used instead of a singular CIOU loss function. In addition, the lightweight Content-Aware Reassembly of Features (CARAFE) replaces the original bilinear interpolation upsampling algorithm, and a spatial pyramid structure is added into the network model’s small target layer. The proposed algorithm undergoes training and validation utilizing the benchmark dataset known as AI-TOD. Compared to speed-oriented YOLOv7-tiny, the mAP0.5 and mAP0.5:0.95 of our improved algorithm increased from 42.0% and 16.8% to 48.7% and 18.9%, representing improvements of 6.7% and 2.1%, respectively, while the detection speed was almost equal to that of YOLOv7-tiny. Furthermore, our method was also tested on a dataset of multi-scale targets, which contains small targets, medium targets, and large targets. The results demonstrated that mAP0.5:0.95 increased from “9.8%, 54.8%, and 68.2%” to “12.6%, 55.6%, and 70.1%” for detection across different scales, indicating improvements of 2.8%, 0.8%, and 1.9%, respectively. In summary, the presented method improves detection metrics for micro-targets in various scenarios while satisfying the requirements of detection speed in a real-time system.
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| 39. |
- Zhou, Huiquan, et al.
(författare)
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Predicting Emergency Department Utilization among Older Hong Kong Population in Hot Season: A Machine Learning Approach
- 2022
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Ingår i: Information. - : MDPI. - 2078-2489. ; 13:9
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Tidskriftsartikel (refereegranskat)abstract
- Previous evidence suggests that temperature is associated with the number of emergency department (ED) visits. A predictive system for ED visits, which takes local temperature into account, is therefore needed. This study aimed to compare the predictive performance of various machine learning methods with traditional statistical methods based on temperature variables and develop a daily ED attendance rate predictive model for Hong Kong. We analyzed ED utilization among Hong Kong older adults in May to September from 2000 to 2016. A total of 103 potential predictors were derived from 1- to 14-day lag of ED attendance rate and meteorological and air quality indicators and 0-day lag of holiday indicator and month and day of week indicators. LASSO regression was used to identify the most predictive temperature variables. Decision tree regressor, support vector machine (SVM) regressor, and random forest regressor were trained on the selected optimal predictor combination. Deep neural network (DNN) and gated recurrent unit (GRU) models were performed on the extended predictor combination for the previous 14-day horizon. Maximum ambient temperature was identified as a better predictor in its own value than as an indicator defined by the cutoff. GRU achieved the best predictive accuracy. Deep learning methods, especially the GRU model, outperformed conventional machine learning methods and traditional statistical methods.
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