In our work we focus on knowledge bases providing long tail content, i. We propose a system for source selection which i could be utilized to automatically detect long tail knowledge bases and ii generates aggregated search results that tend to incorporate results from these long tail sources. Starting from the current state-of-the-art we developed components that allowed to adjust the amount of contribution from long tail sources.
As this dataset also favors the most popular sources, systems that include many long tail knowledge bases will yield low performancemeasures. Here, we propose a system where just a few relevant long tail sources are integrated into the list of more popular knowledge bases. Additionally, we evaluated the implications of an uncooperative setting, where only minimal information of the sources is available to the federated search system.
Our work is intended to steer the development of federated search systems that aim at increasing the diversity and coverage of the aggregated search result. In this work, we address the problem of recommending jobs touniversity students. For this, we explore the impact of using itemembeddings for a content-based job recommendation system. Fur-thermore, we utilize a model from human memory theory to integratethe factors of frequency and recency of job posting interactions forcombining item embeddings.
We evaluate our job recommendationsystem on a dataset of the Austrian student job portal Studo usingprediction accuracy, diversity as well as adapted novelty, which isintroduced in this work. We find that utilizing frequency and recencyof interactions with job postings for combining item embeddingsresults in a robust model with respect to accuracy and diversity, butalso provides the best adapted novelty results.
Semiconductor manufacturing processes critically depend on hundreds of highly complex process steps, which may cause critical deviations in the end-product. Hence, a better understanding of wafer test data patterns, which represent stress tests conducted on devices in semiconductor material slices, may lead to an improved production process. However, the shapes and types of these wafer patterns, as well as their relation to single process steps, are unknown. In a first step to address these issues, we tailor and apply a variational auto-encoder VAE to wafer pattern images.
We find the VAE's generator allows for explorative wafer pattern analysis, andits encoder provides an effective dimensionality reduction algorithm, which, in a clustering application, performs better than several baselines such as t-SNE and yields interpretable clusters of wafer patterns. Today's data amount is significantly increasing. A strong buzzword in research nowadays is big data.
Therefore the chemistry student has to be well prepared for the upcoming age where he does not only rule the laboratories but is a modeler and data scientist as well. This tutorial covers the very basics of molecular modeling and data handling with the use of Python and Jupyter Notebook. It is the first in a series aiming to cover the relevant topics in machine learning, QSAR and molecular modeling, as well as the basics of Python programming. These technologies are particularly important for manufacturing sites, where complex processes are coupled with large amounts of data, for example in chemical and steel industry.
This data originates from sensors, processes. Typical application of these technologies is related to predictive maintenance and optimisation of production processes. In practice the data is often unstructured Gandomi and Haider, and a lot of resources are devoted to cleaning and preparation, but also to understanding causalities and relevance among features.
The latter one requires domain knowledge, making big data projects not only challenging from a technical perspective, but also from a communication perspective. Therefore, there is a need to rethink the big data concept among researchers and manufacturing experts including topics like data quality, knowledge exchange and technology required. The scope of this presentation is to present the main pitfalls in applying big data technologies amongst users from industry, explain scaling principles in big data projects, and demonstrate common challenges in an industrial big data project.
However, not all instances and topics receive the same amount of attention, as some thrive and achieve self-sustaining levels of activity while others fail to attract users and either never grow beyond being a small niche community or become inactive. We aim to empower community managers with quantitative methods for them to better understand, control and foster their communities, and thus contribute to making the Web a more efficient place to exchange information.
We find four distinct types of user activity temporal patterns, which vary primarily according to the users' activity frequency. Vibrotactile skin-reading uses wearable vibrotactile displays to convey dynamically generated textual information. Such wearable displays have potential to be used in a broad range of applications. Nevertheless, the reading process is passive, and users have no control over the reading flow. To compensate for such drawback, this paper investigates what kind of interactions are necessary for vibrotactile skin reading and the modalities of such interactions.
An interaction concept for skin reading was designed by taking into account the reading as a process. We performed a formative study with 22 participants to assess reading behaviour in word and sentence reading using a six-channel wearable vibrotactile display. Our study shows that word based interactions in sentence reading are more often used and preferred by users compared to character-based interactions and that users prefer gesture-based interaction for skin reading. Finally, we discuss how such wearable vibrotactile displays could be extended with sensors that would enable recognition of such gesture-based interaction.
This paper contributes a set of guidelines for the design of wearable haptic displays for text communication. In this paper, we study the process of opinion dynamics and consensus building inonline collaboration systems, in which users interact with each other followingtheir common interests and their social pro les. Speci cally, we are interested inhow users similarity and their social status in the community, as well as theinterplay of those two factors inuence the process of consensus dynamics.
Forour study, we simulate the di usion of opinions in collaboration systems using thewell-known Naming Game model, which we extend by incorporating aninteraction mechanism based on user similarity and user social status. Weconduct our experiments on collaborative datasets extracted from the Web. Our ndings reveal that when users are guided by their similarity to other users, theprocess of consensus building in online collaboration systems is delayed. Asuitable increase of inuence of user social status on their actions can in turnfacilitate this process.
In summary, our results suggest that achieving an optimalconsensus building process in collaboration systems requires an appropriatebalance between those two factors. Production companies typically have not utilized video content and video technology in factory environ-ments to a significant extent in the past. However, the current Industry 4. Infrastructure and machines get connected to central manufacturing execution systems in digitization and datafication efforts. In the realm of this fourth industrial revolution, companies are encouraged to revisit their strategy regarding video-based applications as well.
This paper discusses the current situation and selected aspects of opportu-nities and challenges of video technology that might enable added value in such environments. The project is following a multi-disciplinary, industry-driven approach to the analysis and understanding of learner data in order to personalize, accelerate and improve informal learning processes. Learning Analytics and Educational Data Mining traditionally relate to the analysis and exploration of data coming from learning environments, especially to understand learners' behaviours.
However, studies have for a long time demonstrated that learning activities happen outside of formal educational platforms, also. This includes informal and collective learning usually associated, as a side effect, with other social environments and activities. Relying on real data from a commercially available platform, the aim of AFEL is to provide and validate the technological grounding and tools for exploiting learning analytics on such learning activities. This will be achieved in relation to cognitive models of learning and collaboration, which are necessary to the understanding of loosely defined learning processes in online social environments.
Applying the skills available in the consortium to a concrete set of live, industrial online social environments, AFEL will tackle the main challenges of informal learning analytics through 1 developing the tools and techniques necessary to capture information about learning activities from not necessarily educational online social environments; 2 creating methods for the analysis of such informal learning data, based on combining feature engineering and visual analytics with cognitive models of learning and collaboration; and 3 demonstrating the potential of the approach in improving the understanding of informal learning, and the way it is better supported; 4 evaluate all the former items in real world large scale applications and platforms.
Traditionally, professional learning for senior professionalsis organized around faceface trainings. Virtual trainingsseem to offer an opportunity to reduce costs related to traveland travel time. In this paper we present a comparative casestudy that investigates the differences between traditionalfaceface trainings in physical reality, and virtualtrainings via WebEx. Our goal is to identify how the way ofcommunication impacts interaction between trainees,between trainees and trainers, and how it impactsinterruptions.
We present qualitative results fromobservations and interviews of three cases in differentsetups traditional classroom, web-based with allparticipants co-located, web-based with all participants atdifferent locations and with overall 25 training participantsand three trainers. The study is set within one of the BigFour global auditing companies, with advanced seniorauditors as learning cohort.
Collection of environmental datasets recorded with Tinkerforge sensors and used in the development of a bachelor thesis on the topic of frequent pattern mining.
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The data was collected in several locations in the city of Graz, Austria, as well as an additional dataset recorded in Santander, Spain. This paper investigates the effects of using passive haptic learning to train the skill of comprehending text from vibrotactile patterns. The method of transmitting messages, skin-reading, is effective at conveying rich information but its active training method requires full user attention, is demanding, time-consuming, and tedious. Passive haptic learning offers the possibility to learn in the background while performing another primary task.
We present a study investigating the use of passive haptic learning to train for skin-reading. In the context of the Internet of Things IoT , every device have sensing and computing capabilities to enhance many aspects of human life. There are more and more IoT devices in our homes and at our workplaces, and they still depend on human expertise and intervention for tasks as maintenance and re configuration. In the Big Data era, people can access vast amounts of information, but often lack the time, strategies and tools to efficiently extract the necessary knowledge from it.
Research and innovation staff needs to effectively obtain an overview of publications, patents, funding opportunities, etc. The MOVING platform enables its users to improve their information literacy by training how to exploit data mining methods in their daily research tasks. Through a novel integrated working and training environment, the platform supports the education of data-savvy information professionals and enables them to deal with the challenges of Big Data and open innovation.
Becoming a data-savvy professional requires skills and competencesin information literacy, communication and collaboration, and content creationin digital environments. In this paper, we present a concept for automatic learningguidance in relation to an information literacy curriculum. The learning guidanceconcept has three components: Firstly, an open learner model in terms of an informationliteracy curriculum is created. Based on the data collected in the learnermodel, learning analytics is used in combination with a corresponding visualizationto present the current learning status of the learner.
Secondly, reflectionprompts in form of sentence starters or reflective questions adaptive to the learnermodel aim to guide learning. Thirdly, learning resources are suggested that arestructured along learning goals to motivate learners to progress. The main contributionof this paper is to discuss what we see as main research challenges withrespect to existing literature on open learner modeling, learning analytics, recommendersystems for learning, and learning guidance. Knowledge Workers and Teachers. Learning analytics deals with tools and methods for analyzing anddetecting patterns in order to support learners while learning in formal as wellas informal learning settings.
In this work, we present the results of two focusgroups in which the effects of a learning resource recommender system and adashboard based on analytics for everyday learning were discussed from twoperspectives: Our findings show that the advantages of analytics for everydaylearning are three-fold: Information-seeking tasks with learning or investigative purposes are usually referred to as exploratory search. Exploratory search unfolds as a dynamic process where the user, amidst navigation, trial-and-error and on-the-fly selections, gathers and organizes information resources.
A range of innovative interfaces with increased user control have been developed to support exploratory search process. In this work we present our attempt to increase the power of exploratory search interfaces by using ideas of social search, i. Social search technologies are highly popular nowadays, especially for improving ranking.
However, current approaches to social ranking do not allow users to decide to what extent social information should be taken into account for result ranking.
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This paper presents an interface that integrates social search functionality into an exploratory search system in a user-controlled way that is consistent with the nature of exploratory search. The interface incorporates control features that allow the user to i express information needs by selecting keywords and ii to express preferences for incorporating social wisdom based on tag matching and user similarity.
The interface promotes search transparency through color-coded stacked bars and rich tooltips. In an online study investigating system accuracy and subjective aspects with a structural model we found that, when users actively interacted with all its control features, the hybrid system outperformed a baseline content-based-only tool and users were more satisfied.
With different social media and commercial platforms, users express their opinion about products in a textual form. Automatically extracting the polarity i. Different approaches for tackling the problem, have been suggested mainly using syntactic features. In this paper we propose a novel approach by employing the semantic information of grammatical unit called preposition.
We try to derive the target of the review from the summary information, which serves as an input to identify the proposition in it. Our implementation relies on the hypothesis that the proposition expressing the target of the summary, usually containing the main polarity information. In this work, we propose a content-based recommendation approach to increase exposure to opposing beliefs and opinions. Our aim is to help provide users with more diverse viewpoints on issues, which are discussed in partisan groups from different perspectives.
Since due to the backfire effect, people's original beliefs tend to strengthen when challenged with counter evidence, we need to expose them to opposing viewpoints at the right time. The preliminary work presented here describes our first step into this direction. As illustrative showcase, we take the political debate on Twitter around the presidency of Donald Trump. Background An actual challenge within the sentimentanalysis research area is the extraction of polarity valuesassociated with specic aspects or opinion targets contained in user generated content.
This task, calledaspect-based sentiment analysis bring new challengeslike the disambiguation of words' role within text andthe inference of correct polarity values based on thedomain in which a text occurs. The former requiresstrategies able to understand how each word is usedin a specic context in order to annotate it as aspector not. The latter need to be addressed with unsupervisedsolutions in order to make a system ecient forreal-time tasks and at the same time exible in order toadopt it in any domain without requiring the trainingof sentiment models.
Finally, the deployment of suchsystem into real-world scenarios needs the developmentof usable solutions for accessing and analyzing data. Methods This paper presents the ReUS platform: Results The ReUS platform has been validated from aquantitative and qualitative perspectives. First, the aspectextraction and polarity inference capabilities have been evaluated on three dataset used in likewise editionsof SemEval. Second, a user group has been invitedto judge the usability of the platform. Conclusion The developed platform demonstrated to besuitable for being used into real-world scenarios requiring i the capability of processing real-time opinionbaseddocuments streams and ii the availability ofusable facilities for analyzing and visualizing collecteddata.
Examples of possible analysis and visualizationsincludes the presentation of lists ranking aspects bytheir importance of by their polarity values computedwithin the whole data repository. This kind of analysisenables, for instance, the discovery of product issues. As open access OA to publications continues to gather momentum, we should continuously question whether it is moving in the right direction.
A novel intervention in this space is the creation of OA publishing platforms commissioned by funding organizations. Examples include those of the Wellcome Trust and the Gates Foundation, as well as recently announced initiatives from public funders like the European Commission and the Irish Health Research Board. As the number of such platforms increases, it becomes urgently necessary to assess in which ways, for better or worse, this emergent phenomenon complements or disrupts the scholarly communications landscape.
This article examines ethical, organizational, and economic strengths and weaknesses of such platforms, as well as usage and uptake to date, to scope the opportunities and threats presented by funder OA platforms in the ongoing transition to OA. However, the article identifies key areas of concern about the potential for unintended consequences, including the appearance of conflicts of interest, difficulties of scale, potential lock-in, and issues of the branding of research.
The article ends with key recommendations for future consideration which include a focus on open scholarly infrastructure. Open peer review OPR , as with other elements of open science and open research, is on the rise. It aims to bring greater transparency and participation to formal and informal peer review processes.
How do authors or reviewers approach OPR? And what pitfalls and opportunities should you look out for? Here, we propose ten considerations for OPR, drawing on discussions with authors, reviewers, editors, publishers and librarians, and provide a pragmatic, hands-on introduction to these issues. We cover basic principles and summarise best practices, indicating how to use OPR to achieve best value and mutual benefits for all stakeholders and the wider research community.
Similar to the creation of the internet, today the world is captivated by a new phenomenon that is considered to produce substantial impact on economic life. We are talking about the Blockchain technology, which was initially introduced as the technological backbone of the cryptocurrency Bitcoin, and is in a simplified form representing a decentralized data storage system within a peer-to-peer network.
As Blockchains can be implemented in many areas of life, there are continuously emerging new ideas of utilization. However, fully organizing legal relationships by Smart Contracts in the near future also raises doubt on the consistency of connected processes with the law. This article examines Smart Contracts from a legal perspective, specifically explaining its place in existing Austrian contract law.
This short note presents results about the symmetric Jensen-Shannon divergence between two discrete mixture distributions p1 and p2. Sensory substitution has been a research subject for decades, and yet its applicability outside of the research is very limited. Thus creating scepticism among researchers that a full sensory substitution is not even possible [8]. In this paper, we do not substitute the entire perceptual channel. Instead, we follow a different approach which reduces the captured information drastically. We present concepts and implementation of two mobile applications which capture the user's environment, describe it in the form of text and then convey its textual description to the user through a vibrotactile wearable display.
The applications target users with hearing and vision impairments. Learning technologies offer opportunities for users to enhance and to personalize selfregulatedlearning activities. In this paper, we present the results of a laboratory study that coversthe user evaluation of the AFEL Didactalia app, which analyzes everyday learning activities oflearners, extracts learning scopes and trajectories, and provides personalized recommendationsof learning resources as well as an interactive visualization of learning activities. The related behaviordata was tracked and analyzed by the AFEL learning tool.
After completing the two tasks,participants received an introduction to the tool and explored them as well. The results suggest asatisfactory experience with the tool and provide insights on the potential benefits, as well asaspects to be improved for further development. We discuss our findings and the next steps ofthe investigation in detail.
User-based Collaborative Filtering CF is one of the most popularapproaches to create recommender systems. One solution is to incorporateadditional information into the recommendation process such asexplicit trust scores that are assigned by users to others or implicittrust relationships that result from social connections betweenusers. Such relationships typically form a very sparse trust network,which can be utilized to generate recommendations for users basedon people they trust.
In our work, we explore the use of a measurefrom network science, i. The project Flexible Intralogistics for Future Factories FlexIFF investigates human-robot collaboration in intralogistics teams in the manufacturing industry, which form a cyber-physical system consisting of human workers, mobile manipulators, manufacturing machinery, and manufacturing information systems. The right information at the right time is key for making this collaboration successful.
FlexIFF will provide useful, well-tested, and sophisticated solutions for cyberphysicals systems in intralogistics, with humans and robots making the most of their strengths, working collaboratively and helping each other. The micro-blogging platform Twitter allows its nearly million monthly active users to build a network of follower connections to other Twitter users i. With this feature, Twitter has become one of the most popular social networks on the Web and was also the first platform that offered the concept of hashtags.
Hashtags are freely-chosen keywords, which start with the hash character, to annotate, categorize and contextualize Twitter posts i. Although hashtags are widely accepted and used by the Twitter community, the heavy reuse of hashtags that are popular in the personal Twitter networks i. These filter bubble effects are also highly associated with the concept of confirmation bias, which is the tendency to favor and reuse information that confirms personal preferences. One example would be a Twitter user who is interested in political tweets of US president Donald Trump. Depending on the hashtags used, the user could either be stuck in a pro-Trump e.
Therefore, the goal of this paper is to study confirmation bias and filter bubble effects in hashtag usage on Twitter by treating the reuse of hashtags as a phenomenon that fosters confirmation bias. Co-Creation methods for interactive computer systems design are by now widely accepted as part of the methodological repertoire in any software development process. They are not tangible like featuresin a tool and desired effects are harder to be explained or understood. Therefore, we propose an it-erative simulation-based Co-Design approach that allows to Co-Create Algo-rithms together with the domain professionals by making their assumptions and effects observable.
The proposal is a methodological idea for discussion within the EC-TEL community, yet to be applied in a research practice. The number of scientific publications has rapidly increased over the last decades and still shows asteady growth. In addition, medical scientists and practitioners often have to deal with multiplesystems and databases for literature search.
This results in information overload and professionalshardly being able to keep up to date with the latest scientific publications in their limited free time. Approximately five to eightpersons have been involved in the collaborative design of the medtool over a period of sixmonths from paper to software prototyping.
For this purpose, workshops and interviews wereconducted with relevant stakeholders such as the domain professionals, developers and researchers. Instead of crawling through multiple systems like research gate, google scholar or publisher websites,our study indicates, that HC professionals require an easy-to-use tool. It must be in line with theirbusy work life and give access to literature in one place in reasonable extent. In consequence,med allows for a straight forward definition of the search scope by entering a few keywords andproviding a forecast of the expected number of papers per week.
The identified literature ispresented in the well-adopted mailbox format on mobiles,tablets and personal computers frompredefined literature systems and databases. This way, med helps researcher to better cope with their workload: Cancer is one of the most uprising diseases in our modern society and is defined by an uncontrolled growth of tissue. This growth is caused by mutation on the cellular level. In this thesis, a data-mining workflow was developed to find these responsible genes among thousands of irrelevant ones in three microarray datasets of different cancer types by applying machine learning methods such as classification and gene selection.
In this work, four state-of-the-art selection algorithms are compared with a more sophisticated method, termed Stacked-Feature Ranking SFR , further increasing the discriminatory ability in gene selection. In this paper, we present work-in-progress on applying user pre-filtering to speed up and enhance recommendations based on Collab-orative Filtering. We propose to pre-filter users in order to extracta smaller set of candidate neighbors, who exhibit a high numberof overlapping entities and to compute the final user similaritiesbased on this set.
To realize this, we exploit features of the high-performance search engine Apache Solr and integrate them into ascalable recommender system. We have evaluated our approachon a dataset gathered from Foursquare and our evaluation resultssuggest that our proposed user pre-filtering step can help to achieveboth a better runtime performance as well as an increase in overallrecommendation accuracy. Furthermore,AFEL-REC can cope with any kind of data that is present in sociallearning environments such as resource metadata, user interactionsor social tags.
We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker.
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Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. Our model helps users to efficiently identify interesting time-series patterns.
This paper aims to identify self-regulation strategies from students' interactions with the learning management system LMS. We used learning analytics techniques to identify metacognitive and cognitive strategies in the data. We define three research questions that guide our studies analyzing i self-assessments of motivation and self regulation strategies using standard methods to draw a baseline, ii interactions with the LMS to find traces of self regulation in observable indicators, and iii self regulation behaviours over the course duration.
The results show that the observable indicators can better explain self-regulatory behaviour and its influence in performance than preliminary subjective assessments. However, visualizations have shown to be effective in dealing with huge datasets: But, creating appropriate visual representations of data is also challenging: A user places interests on several aspects of a visualization, the task or problem it helps to solve, the operations it permits, or the features of the dataset it represents.
This paper concentrates on characterizing user preferences, in particular: We consider three sources corresponding to different aspects of interest: We investigate user-provided input based on these sources collected with a crowd-sourced study. Firstly, information-theoretic measures are applied to each source to determine the efficiency of the input in describing user preferences and visualization contents user and item models. Secondly, the practicability of each input is evaluated with content-based recommender system. The overall methodology and results contribute methods for design and analysis of visual recommender systems.
Enabling interactive access to multimedia content and evaluating content-consumption behaviors and experiences involve several different research areas, which are covered at many different conferences. For four years, the Workshop on Interactive Content Consumption WSICC series offered a forum for combining interdisciplinary, comprehensive views, inspiring new discussions related to interactive multimedia.
Here, the authors reflect on the outcome of the series. In manufacturing environments today, automated machinery works alongside human workers. In many cases computers and humans oversee different aspects of the same manufacturing steps, sub-processes, and processes.
This paper identifies and describes four feedback loops in manufacturing and organises them in terms of their time horizon and degree of automation versus human involvement. The data flow in the feedback loops is further characterised by features commonly associated with Big Data.
Velocity, volume, variety, and veracity are used to establish, describe and compare differences in the data flows. However, one problem of large SPLOMs is that typically not all views are potentially relevant to a given analysis task or user. The matrix itself may contain structured patterns across the dimensions, which could interfere with the investigation for unexplored views. We introduce a new concept and prototype implementation for an interactive recommender system supporting the exploration of large SPLOMs based on indirectly obtained user feedback from user eye tracking.
Our system records the patterns that are currently under exploration based on gaze times, recommending areas of the SPLOM containing potentially new, unseen patterns for successive exploration. We use an image-based dissimilarity measure to recommend patterns that are visually dissimilar to previously seen ones, to guide the exploration in large SPLOMs. The dynamic exploration process is visualized by an analysis provenance heatmap, which captures the duration on explored and recommended SPLOM areas.
We demonstrate our exploration process by a user experiment, showing the indirectly controlled recommender system achieves higher pattern recall as compared to fully interactive navigation using mouse operations. This paper provides an overview of the history, technologies and concepts of Industry 4. One of the biggest challenges to implementing the Industry 4. These issues have been addressed in the semiconductor industry since the early s and some solutions have become well-established standards.
Hence, the semiconductor industry can provide guidelines for a transition towards Industry 4. In this work, the methodologies of Industry 4. Based on a thorough literature review and experiences from the semiconductor industry, we offer implementation recommendations for Industry 4. In our research we explore representing the state of production machines using a new nature metaphor, called BioIoT. In this paper we describe a study with twelve participants in which sensory information of a coffee machine is encoded in a virtual tree. The study highlights as directions for follow-up research personalization, intelligibility vs representational power, limits of the metaphor, and immersive visualization.
Food recommenders have the potential to positively in uence theeating habits of users. To achieve this, however, we need to understandhow healthy recommendations are and the factors whichin uence this. Focusing on two approaches from the literature single item and daily meal plan recommendation and utilizing alarge Internet sourced dataset from Allrecipes. First, we analyze the healthiness of Allrecipes. Second,we investigate user interaction patterns and how these relate to thehealthiness of recipes. Third, we experiment with both recommendationapproaches.
Our results indicate that overall the recipes inthe collection are quite unhealthy, but this varies across categorieson the website. Users in general tend to interact most often with theleast healthy recipes. Recommender algorithms tend to score popularitems highly and thus on average promote unhealthy items. Thiscan be tempered, however, with simple post- ltering approaches,which we show by experiment are better suited to some algorithmsthan others.
Similarly, we show that the generation of meal planscan dramatically increase the number of healthy options open tousers. One of the main ndings is, nevertheless, that the utilityof both approaches is strongly restricted by the recipe collection. Based on our ndings we draw conclusions how researchers shouldattempt to make food recommendation systems promote healthynutrition. Creative group work can be supported by collaborative search and annotation of Web resources. In this setting, it is important to help individuals both stay fluent in generating ideas of what to search next i.
Based on a model of human memory, we hypothesize that sharing search results with other users, such as through bookmarks and social tags, prompts search processes in memory, which increase ideational fluency, but decrease the consistency of annotations, e. To balance this tradeoff, we suggest the tag recommender SoMe, which is designed to simulate search of memory from user-specific tag-topic associations. We conclude that sharing search results supports group creativity by increasing the ideational fluency, and that SoMe helps balancing the evidenced fluency-consistency tradeoff.
This paper describes a novel visual metaphor to communicate sensor information of a connected device. The Internet of Things aims to extend every device with sensing and computing capabilities. A byproduct is that even domestic machines become increasingly complex, tedious to understand and maintain. This paper presents a prototype instrumenting a coffee machine with sensors. The machine streams the sensor data, which is picked up by an augmented reality application serving a nature metaphor.
The nature metaphor, BioAR, represents the status derived from the coffee machine sensors in the features of a 3D virtual tree. The tree is meant to pass for a living proxy of the machine it represents. The metaphor, shown either with AR or a simple holographic display, reacts to the user manipulation of the machine and its workings. A first user study validates that the representation is correctly understood, and that it inspires affect for the machine. A second user study validates that the metaphor scales to a large number of machines.
Thereby,the digitization and the interlinking does not only affects themachines and IT infrastructure, rather also the employees areaffected [3]. The employees have to acquire more and morecomplex knowledge within a shorter period of time. To copewith this challenge, the learning needs to be integrated into thedaily work practices, while the learning communities shouldmap the organizational production networks [2].
Such learningnetworks support the knowledge exchange and joint problemsolving together with all involved parties [4]. However, insuch communities not all involved actors are known and hencesupport to find the right learning material and peers is needed. Nowadays, many different learning environments are usedin the industry. Their complexity makes it hard to understandwhether the system provides an optimal learning environment.
The large number of learning resources, learners and theiractivities makes it hard to identify potential problems inside alearning environment. Since the human visual system providesenormous power for discovering patterns from data displayedusing a suitable visual representation [5], visualizing such alearning environment could provide deeper insights into itsstructure and activities of the learners.
Our goal is to provide a visual framework supporting theanalysis of communities that arise in a learning environment. To specify the current understanding of business models in the realm of Big Data, we used a qualitative approach analysing 25 Big Data projects spread over the domains of Retail, Energy, Production, and Life Sciences, and various company types SME, group, start-up, etc.
The in-depth analysis of time series has gained a lot of re-search interest in recent years, with the identification of pe-riodic patterns being one important aspect. There exist only a few al-gorithms for automatic season length approximation. Manyof these rely on simplifications such as data discretization. This paper presents an algorithm for season length detec-tion that is designed to be sufficiently reliable to be used inpractical applications.
This is followed by analyzing the distances betweenzeros in the directly corresponding autocorrelation function. Our algorithm was tested against a comparable algorithmand outperformed it by passing out of tests, whilethe existing algorithm passed 83 tests. The robustness of ourmethod can be jointly attributed to both the algorithmic ap-proach and also to design decisions taken at the implemen-tational level. The combination of different knowledge bases in thefield of information retrieval is called federated or aggregated search.
It has several benefits over single source retrieval but poses some challenges as well. This work focuses on the challenge of result aggregation; especially in a setting where the final result list should include a certain degree of diversity and serendipity. Both concepts have been shown to have an impact on how user perceive an information retrieval system.
In particular, we want to assess if common procedures for result list aggregation can be utilized to introduce diversity and serendipity.
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Furthermore, we study whether a blocking or interleaving for result aggregation yields better results. In a cross vertical aggregated search the so-called verticalscould be news, multimedia content or text. Block ranking is one approach to combine such heterogeneous result. It relies on the idea that these verticals are combined into a single result list as blocks of several adjacent items.
An alternative approach for this is interleaving. Here the verticals are blended into one result list on an item by item basis, i. To generate the diverse and serendipitous results we reliedon a query reformulation technique which we showed to be beneficial to generate diversified results in previous work. To conduct this evaluation we created a dedicated dataset. This dataset served as a basis for three different evaluation settings on a crowd sourcing platform, with over participants.
Our results show that query based diversification can be adapted to generate serendipitous results in a similar manner. Further, we discovered that both approaches, interleaving and block ranking, appear to be beneficial to introduce diversity and serendipity. Though it seems that queries either benefit from one approach or the other but not from both.
Despite the ubiquity of learning in the everyday life of most workplaces, the learning analytics community only has paid attention to such settings very recently. One probable reason for this oversight is the fact that learning in the workplace is often informal, hard to grasp and not univocally defined. This paper summarizes the state of the art of Workplace Learning Analytics WPLA , extracted from a systematic literature review of five academic databases as well as other known sources in the WPLA community.
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We end the paper by discussing opportunities for future work in this emergent field. Aktuelle Untersuchungen zeigen einerseits auf, dass der Mensch weiterhin eine zentrale Rolle in der Industrie spielt. Der Erfolg einer Industrie 4. Research on information system IS adoption and resistance has accumulatedsubstantial theoretical and managerial knowledge. Surprisingly, the paradox that end userssupport and at the same time resist use of an IS has received relatively little attention. Theinvestigation of this puzzle, however, is important to complement our understanding ofresistant behaviours and consequently to strengthen the explanatory power of extanttheoretical constructs on IS resistance.
We investigate an IS project within the healthcare Basierend auf der fortschreitenden Digitalisierung nimmt das Angebotan strukturierten und unstrukturierten Daten in den unterschiedlichen Bereichen der Wirtschaft rasant zu. Recommender systems are acknowledged as an essential instrumentto support users in finding relevant information. However,the adaptation of recommender systems to multiple domain-specificrequirements and data models still remains an open challenge. Inthe present paper, we contribute to this sparse line of research withguidance on how to design a customizable recommender systemthat accounts for multiple domains with heterogeneous data.
Usingconcrete showcase examples, we demonstrate how to setup amulti-domain system on the item and system level, and we reportevaluation results for the domains of i LastFM, ii FourSquare,and iii MovieLens. We believe that our findings and guidelinescan support developers and researchers of recommender systemsto easily adapt and deploy a recommender system in distributedenvironments, as well as to develop and evaluate algorithms suitedfor multi-domain settings.
Financial auditors routinely search internal as well as public knowledge bases as part of the auditing process. Efficient search strategies are crucial for knowledge workers in general and for auditors in particular. Modern search technology quickly evolves; and features beyond keyword search like fac-etted search or visual overview of knowledge bases like graph visualisations emerge. It is therefore desirable for auditors to learn about new innovations and to explore and experiment with such technologies.
In this paper, we present a reflection intervention concept that intends to nudge auditors to reflect on their search behaviour and to trigger informal learning in terms of by trying out new or less frequently used search features. The reflection intervention concept has been tested in a focus group with six auditors using a mockup.
Foremost, the discussion centred on the timing of reflection interventions and how to raise mo-tivation to achieve a change in search behaviour. In this paper, we present two design cycles for an online platform with ICT-enabled tooling that supports business model innovation by SMEs. The platform connects the needs of the SMEs regarding BMI with tools that can help to solve those needs and questions. Recommender systems have become important tools to supportusers in identifying relevant content in an overloaded informationspace. To ease the development of recommender systems, a numberof recommender frameworks have been proposed that serve a widerange of application domains.
Our TagRec framework is one of thefew examples of an open-source framework tailored towards developingand evaluating tag-based recommender systems. The growing dissatisfaction with the traditional scholarly communication process and publishing practices as well as increasing usage and acceptance of ICT and Web 2. The EU-funded project OpenUP addresses key aspects and challenges of the currently transforming science landscape and aspires to come up with a cohesive framework for the review-disseminate-assess phases of the research life cycle that is fit to support and promote open science.
The objective of this paper is to present first results and conclusions of the landscape scan and analysis of alternative peer review, altmetrics and innovative dissemination methods done during the first project year. A Case Study of Ach So! This study explored the application scenarios of a mobile app called Ach So! The mobile application was used forpiloting new technology-enhanced learning practices in vocational apprenticeship trainingat construction sites in Finland and in a training center in Germany.
Semi-structured focusgroup interviews were conducted after the pilot test periods. The interview data served asthe data source for the concept-driven framework analysis that employed theoretical In online social learning environments, tagging has demonstratedits potential to facilitate search, to improve recommendationsand to foster reflection and learning. Studieshave shown that shared understanding needs to be establishedin the group as a prerequisite for learning. We hypothesisethat this can be fostered through tag recommendationstrategies that contribute to semantic stabilization.
In this study, we investigate the application of two tag recommendersthat are inspired by models of human memory: BLL models the frequency and recency of tag use while Minervais based on frequency of tag use and semantic context. We test the impact of both tag recommenders on semanticstabilization in an online study with 56 students completinga group-based inquiry learning project in school.
Testing for the accuracyof the different recommenders revealed that algorithms usingfrequency counts such as BLL performed better whenindividual tags were recommended. When group tags wererecommended, the Minerva algorithm performed better. Hashtags have become a powerful tool in social platformssuch as Twitter to categorize and search for content, and tospread short messages across members of the social network.
In this paper, we study temporal hashtag usage practices inTwitter with the aim of designing a cognitive-inspired hashtagrecommendation algorithm we call BLLI,S. Our mainidea is to incorporate the effect of time on i individualhashtag reuse i. Firstly, only temporal usage patternsof past hashtag assignments are utilized and secondly, thesepatterns are combined with a content-based analysis of thecurrent tweet. In both evaluation scenarios, we find not onlythat temporal effects play an important role for both individualand social hashtag reuse but also that our BLLI,S approachprovides significantly better prediction accuracy andranking results than current state-of-the-art hashtag recommendationmethods.
New business opportunities in the digital economy are established when datasets describing a problem, data services solving the said problem, the required expertise and infrastructure come together. For most real-word problems finding the right data sources, services consulting expertise, and infrastructure is difficult, especially since the market players change often.
The Data Market Austria DMA offers a platform to bring datasets, data services, consulting, and infrastructure offers to a common marketplace. The recommender systems included in DMA analyses all offerings, to derive suggestions for collaboration between them, like which dataset could be best processed by which data service. The suggestions should help the costumers on DMA to identify new collaborations reaching beyond traditional industry boundaries to get in touch with new clients or suppliers in the digital domain.
Human brokers will work together with the recommender system to set up data value chains matching different offers to create a data value chain solving the problems in various domains. In its final expansion stage, DMA is intended to be a central hub for all actors participating in the Austrian data economy, regardless of their industrial and research domain to overcome traditional domain boundaries. The rapid development of information technology and the increasing pervasiveness of digitalization represent new challenges to the business world.
The emergence of the so-called fourth industrial revolution and the Internet of Things IoT confronts existing firms with changes in numerous aspects of doing business. Not only information and communication technologies are changing production processes through increasing automation. Digitalization can affect products and services itself. This paper presents how digitalization affects business models of well-established companies in Austria.
Maerg offers his experience on IT and marketing services to help other companies to develop e-commerce businesses. MakoLab is an innovative and rapidly growing IT Company. Makolab is a leader in the web application design, for both Internet and desktop systems. MakoLab provides hosting services for OpenCms solutions. Manex is a network and software engineering company. Since , we have been proposing consulting and development services. Please see our website for more info. I'd be happy to consult you in implementing customized modules and templates, embed other functions like shops or administer your server.
Tourism oriented enterprise level content management solutions based on OpenCms. We also offer high availability through customizable CMS specific web hosting solutions. We offer full service with high quality standards in consulting, design and development. With OpenCms we found a perfect Java based solution, that is flexible enough for the great variety of our customers needs. Medienkonzepte is a consultant, developer and designer specialized in interactive solutions for an outstanding user experience.
Medienkonzepte offers advanced skills to bring a concept to market - our team will not focus only on one aspect of a project but take care for the full range of tasks around a digital solution. NAOS Technologies's function is to provide efficient and cost effective solutions for internet, based on Open Source technologies.
NAOS Technologies supplies products and professional services concerning content management, documents management, E-mailing and web hosting. Neodesy Limited specialises in web-based and mobile solutions. Our experienced consulting and technical team works with the client through all stages of a technology project, from conceptualization and analysis requirements to development, hosting and support. Neodesy's areas of strength include:.
With the aim to build profitable web-applications we guarantee our clients a useful appliance of new technologies. Besides technical and functional criteria we are focussed on the long-term benefits for our clients. Nextres develops and markets the next generation of mission critical business systems to the airline and travel industries. Nextres Onyx airline reservation system lets airlines take advantage of an innovative inventory design, featuring a sophisticated pricing engine and multi-channel distribution.
Nextres' software platform is open source centric, with Linux as operating system, JBoss as application server, Apache as front-end and OpenCms for content management. We are specialists for content management, e-business and customer relationship management. The company provides IT solutions, webdesign and online-marketing for perfect communication and interaction in sales and marketing processes. Our team designs and implements customized IT-solutions and is skilled in OpenCms. Our services include integrated solutions and responsive full service support specializing in following areas: Svetoslav Rankov and Miss Ljubomila Alexandrova.
The company integrates the skills of experienced IT specialists, the enthusiasm of young software developers, the creativity of good designers, and the precision of financial consultants. Our mission is to keep our clients ahead informed at all stages of the development cycle, to obtain a complete understanding of our client's businesses and goals and to satisfy not only their wishes but also their needs Our Proposition - We fully understand Your requirements and further create custom solutions that deliver optimum value to Your enterprise by yielding an excellent ROI.
The right mixture of knowledge, expertise, education, creativity, and cultural background contributes for our successful business. OpenCms with it simple and flexible design, clean and true ideas, written in Java language and use of the Java EE platform is a very special professional level CMS for us.
Open Answers offers OpenCms hosting and integration services. We are a Spanish company leader in providing IT consulting and global solutions based on Linux platform and Open Source software. We offer to our customers and partners our value-added services and highly qualified personnel, so we are involved in many critical projects with some of the biggest companies and public agencies in Spain. Opensoft is a Portuguese IT company specialized in consultancy, implementation and support of technological solutions.
One of its main areas of intervention is the development of high availability Web Solutions. We can offer much experience with OpenCms projects. A talented team of developers and consultants combines deep technical expertise with a strong end-user focus to streamline content-driven business processes across the value chain and develop the IT solutions to support them. Pomegranate Software offers development, consulting and enterprise scale hosting services for OpenCms. We offer several OpenCms modules: Proteon Development is a full-service solution provider that provides design, development and hosting in the broadest sense.
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Web Site Portal, evolved services and advanced technological solutions. We have a large record of proven expertise in the following industries: Our core competences are consulting, conception, design and software development. The company is focused on professional web page development and providing internet and intranet solutions for organization working in the different busineses. SIA iPilot is developing OpenCms extensions as also integrating OpenCms in the different applications and providing consulting services for OpenCms installation upgrade and suport for the clients.
SMB, a German company specialized on Enterprise Java Technologies, is providing the full range of services from consultancy to software development. KG offers a wide range of services from individual software development to supporting different open source technologies like OpenCms. Synyx has been supporting OpenCms for several years during which many large projects have been implemented using OpenCms. Implementing open source components offers vendor independency and major cost benefits.
Software development, database management, e-business and corporate communication is our business. We design, realize and operate complex and business-critical applications for internet, intranet and extranet and develop innovative concepts and products. Te Lintelo - IT is a small company which provides consulting, development and product integration for wide technology solutions applied to common enterprise processes: Besides the development and professional services in consultancy, technical architecture, project management, software development and hosting we can re design your website UI.
The Reference offers strategic solutions in enterprise content management systems, mobile websites, business KPI definition, search engine marketing and web analytics. Since based at , the Top Solution delivers information technology IT services that provide real business value to our customers. Our fully integrated array of IT staffing, application management outsourcing, and industry-focused IT solutions is backed by a time-tested suite of formal methodologies, a proprietary database of best practices, and a wide range of strategic alliances and partnerships.
TopSolutions development and delivery model is based on blend of onshore and offshore resources carefully balanced to deliver the right combination of cost-effectiveness, quality, and risk management for each client organization. Now the company specialized on Financial Services group and provides a wide range of services to clients in the banking, finance, securities, and insurance sectors.
Alles zusammen generiert Ihren und damit auch unseren Erfolg. Sie erhalten alle Leistungen aus einer Hand oder nutzen flexibel einen Baustein aus unserem Portfolio. UShareSoft's mission is to deliver an easy to use software appliance factory, formally named UForge. It provides a unique SaaS platform to create business ready software appliances. We make it simple for individual developers, communities, and professionals to build from open source software, integrate their applications into ready-to-run automated software such as software appliances, virtual appliances or cloud computing images.
Verinet is an international server hosting provider with 10 locations in Denmark and 34 international locations. Verinet is hosting 6. See our website for OpenCms solutions. Our solutions are endowed with the most advanced technologies for electronic safety, and we have a vast range of products related to the called e-administration, or electronic administration. We make real the utopian promise of other times; we put the administration to the citizen service. We have a wide experience implementing solutions based on OpenCms for diverse governmental organizations and public administrations, relying on a specializing department for the design and development of applications under this technology, with a great amount of tools and own modules.
Whether you are new to global trade or are already multinational, we combine language, technology and web experience for your profit. We are experieneced in OpenCms implementation, and our proven approach to international trade, has paid off time and time again, with varied customers. We have a long experience in the following fields:. For our clients we are both, internet and software developer as well as a partner.
Let our know-how be your advantage! Brand management and creation of mission critical software products is the main potential of 3m5. By now more than 50 companies out of all business sectors such as automotive engineering, media, engine construction, mobile communications, pharmacy and trading benefit from this. Since our requirements concerning robustness are extremely high, we only had positive experience with this Alkacon product. For this reason we manage one of our major OpenCms reference in OpenCms: Furthermore we actively get involved in the OpenCms Developer Community and represented our agency with an own stand on the OpenCms Days and as platin sponsor.
Nov 11, - OpenCms Try out this pre-release version before OpenCms OpenCms 11 brings a major update for the workplace providing better usability and performance. Further enhancements include automated image generation, core support for dynamic contents lists and meta-mappings for SEO. Nov 11, - The OpenCms 11 Beta release notes contain detailed information about the new features and fixes in this beta version. May 17, - While the Alkacon team is currently working hard on OpenCms version 11, another maintenance release May 17, - The OpenCms Do you want to take a quick test drive of OpenCms to get an impression of its great features?
Try out the OpenCms Live Demo website available at http: Deutsch Kontakt Print page Search. To the fast access Main navigation. To the fast access Page content. OpenCms solution providers in Europe Total entries: Coranto Informatica Coranto is specialized in OpenCms solutions. At last we offer hosting in OpenCms environment. Eurelis Eurelis is a french IT company. Abaco Technology Abaco Technology is focused on high grade solutions for e-learning, web applications, and outsourcing.
Arsweb A professionists' group that work together since , to perform advanced web systems, intranets and services. Artigile Ltd The Artigile software development company provides full cycle of software design, development and maintenance. Vision IT-architects IT-architects with knowledge and guts. Bright Interactive Bright Interactive is a full service design and development consultancy that delivers high-quality interactive websites and web applications.
Bright Interactive is based in Brighton in the UK. Clicks and Links Ltd Clicks and Links Ltd undertake general ICT consultancy, project management, develop and deliver web sites and electronic services to the public sector. Cubus Arts Cubus Arts provides a complete range of web-related services: Engineering Ingegneria Informatica spa Engineering Group The Engineering Group is the major Italian player in the Information Technology services, with a complete and integrated offering in system and business integration, outsourcing, proprietary solutions and consulting services.
Gridnine Systems Gridnine Systems — web and software development company with ofices in Russia and Sweden. IS Solutions plc IS Solutions partners with public and private sector clients to support, enhance and extend their online investments. Our key areas of expertise include: Founded in , Ixis Solutions is a informatic company of Majorca Spain. Application Web contents usin OpenCms. Systems, communications, security, formation, JPY plc JPY is a web application developer specialising in content managed and database driven websites, our expertise is in developing the functionality behind complex e-business and ecommerce services.
Knuepfer Verlag Knuepfer Verlag GmbH is projecting, designing, implementing and hosting content management solutions in the field of tourism and its related environment. Manex Manex is a network and software engineering company. Contact me, for any project in Europe. VAS and multi-channel services Open Source migration and consulting Portal development and middleware systems integration Technologies and tools: Medienkonzepte GbR Medienkonzepte is a consultant, developer and designer specialized in interactive solutions for an outstanding user experience.
Neodesy Limited Neodesy Limited specialises in web-based and mobile solutions. Neodesy's areas of strength include: Nextres AB Nextres develops and markets the next generation of mission critical business systems to the airline and travel industries. Opensoft Opensoft is a Portuguese IT company specialized in consultancy, implementation and support of technological solutions. Panoptic NV Panoptic www.