Miracle-dz/UniFAR-FTUs huggingface dataset:
Beyond Resistance: towards a Multi-Level Perspective on Socio-Technical inertia in Organizational Transformation

In the wake of the discourse on Digital Transformation, the phenomenon of socio-technical inertia has caught the attention of Information Systems researchers. As a latently present theme in both practical and theoretical discourse, it is recurrently referred to as an explanation for the large share of transformation initiatives going awry. Despite its prevalence, socio-technical inertia remains an undertheorized, elusive, and ambiguous concept. Based on a structured review, we propose a sensitizing conceptualization of socio-technical inertia as a relative force and a mutual constituent of transformation. From a mechanism-based explanation, we derive an analytical multilevel perspective on the conceptualization and explore this lens in a case study. The case company has transformed its socio-technical macro regime through multiple major alterations of its workplace information technology infrastructure. We illustrate how such alterations in local fields of work contingently evoke inertia. Thereby, inertia is manifested not just as resistance to shifts in the socio-technical regime but also by mechanisms of perfunctory change, through which it undermines transformational efforts. Our findings imply the need for a new angle on inertia in future research and support an alternative managerial conception of transformation to the one currently dominating the discourse.
Balancing User Base and User Stickiness in Platform Scaling

Rapid user base scaling is imperative for a nascent platform venture to generate value and stand out from its competitors. However, even with rapid growth of the user base, there is no guarantee that it will come with user stickiness, that is, a capacity to stimulate extended user interactions and engagement on the platform. We refer to the risk to focus too much on the user scaling at the expense of the user stickiness as the platform scaling trap. To understand how a nascent platform business can overcome the platform scaling trap, we conducted a longitudinal multiple-case study on five short video platforms in China. In this paper, we present our research-in-progress and our anticipated contributions. Our initial findings show that the way that platforms combine digital resources impacts both the growth of the user base and user stickiness. Upon the completion of the research, this study is expected to make a contribution to the platform literature by unpacking the mechanisms of platform scaling through examining digital resource recombination strategies.
Deconstructing multimodality: visual properties and visual context in human semantic processing

Multimodal semantic models that extend linguistic representations with additional perceptual input have proved successful in a range of natural language processing (NLP) tasks. Recent research has successfully used neural methods to automatically create visual representations for words. However, these works have extracted visual features from complete images, and have not examined how different kinds of visual information impact performance. In contrast, we construct multimodal models that differentiate between internal visual properties of the objects and their external visual context. We evaluate the models on the task of decoding brain activity associated with the meanings of nouns, demonstrating their advantage over those based on complete images.
Energy-Based Anomaly Detection A New Perspective for Predicting Software Failures

The ability of predicting failures before their occurrence is a fundamental enabler for reducing field failures and improving the reliability of complex software systems. Recent research proposes many techniques to detect anomalous values of system metrics, and demonstrates that collective anomalies are a good symptom of failure-prone states. In this paper (i) we observe the analogy of complex software systems with multi-particle and network systems, (ii) propose to use energy-based models commonly exploited in physics and statistical mechanics to precisely reveal failure-prone behaviors without training with seeded errors, and (iii) present some preliminary experimental results that show the feasibility of our approach.
Hierarchical Ontology Graph for Solving Semantic Issues in Decision Support Systems

In the context of the development of AI algorithms in natural language processing, tremendous progress has been made in knowledge abstraction and semantic reasoning. However, for answering the questions with complex logic, AI system is still in an early stage. Hierarchical ontology graph is proposed to establish analysis threads for the complex question in order to facilitate AI system to further support in business decision making. The study of selecting the appropriate corpora is intended to improve the data asset management of enterprises.
The Future of Data-driven Personas: A Marriage of Online Analytics Numbers and Human Attributes

The massive volume of online analytics data about customers has led to novel opportunities for user segmentation. However, getting real value from data remains challenging for many organizations. One of the recent innovations in online analytics is data-driven persona generation that can be used to create high-quality human representations from online analytics data. This manuscript (a) summarizes the potential of data-driven persona generation for online analytics, (b) characterizes nine open research questions for data-driven persona generation, and (c) outlines a research agenda for making persona analytics more useful for decision makers.
Methodological Approach of Integrating the Internet of Things with Enterprise Resource Planning Systems

The ubiquitous term “digital transformation” brings in the focus the need for analyzing this concept in the industry today. Specifically, the paper describes how organizations look at one of the digital transformation’s technologies – Internet of Things (IoT) and if they would like to integrate it with the existing information system or it should be mandatory with implementation of Enterprise Resource Planning (ERP) system. The digital transformation surely put the ladder higher and the organizations are researching new options and innovative ways of doing a business in order to survive in the global market. On the other side, the ERP vendors offer methodologies for implementing their product packages. The digital transformation brought the idea of integrating the IoT with ERP system. Studying these methodologies became obvious that no segments are dedicated to integrating the IoT with ERP system. Therefore, we suggest a generic set of steps applicable in existing methodologies of ERP’s implementation.
The CC Chemokine Monocyte Chemotactic Peptide-1 Activates both the Class I p85/p110 Phosphatidylinositol 3-Kinase and the Class II PI3K-C2α*

The cellular effects of MCP-1 are mediated primarily by binding to CC chemokine receptor-2. We report here that MCP-1 stimulates the formation of the lipid products of phosphatidylinositol (PI) 3-kinase, namely phosphatidylinositol 3,4-bisphosphate and phosphatidylinositol 3,4,5-trisphosphate (PI 3,4,5-P3) in THP-1 cells that can be inhibited by pertussis toxin but not wortmannin. MCP-1 also stimulates an increase in the in vitro lipid kinase activity present in immunoprecipitates of the class 1A p85/p110 heterodimeric PI 3-kinase, although the kinetics of activation were much slower than observed for the accumulation of PI 3,4,5-P3. In addition, this in vitro lipid kinase activity was inhibited by wortmannin (IC50 = 4.47 ± 1.88 nm,n = 4), and comparable concentrations of wortmannin also inhibited MCP-stimulated chemotaxis of THP-1 cells (IC50 = 11.8 ± 4.2 nm, n= 4), indicating that p85/p110 PI 3-kinase activity is functionally relevant. MCP-1 also induced tyrosine phosphorylation of three proteins in these cells, and a fourth tyrosine-phosphorylated protein co-precipitates with the p85 subunit upon MCP-1 stimulation. In addition, MCP-1 stimulated lipid kinase activity present in immunoprecipitates of a class II PI 3-kinase (PI3K-C2α) with kinetics that closely resembled the accumulation of PI 3,4,5-P3. Moreover, this MCP-1-induced increase in PI3K-C2α activity was insensitive to wortmannin but was inhibited by pertussis toxin pretreatment. Since this mirrored the effects of these inhibitors on MCP-1-stimulated increases in D-3 phosphatidylinositol lipid accumulation in vivo, these results suggest that activation of PI3K-C2α rather than the p85/p110 heterodimer is responsible for mediating the in vivo formation of D-3 phosphatidylinositol lipids. These data demonstrate that MCP-1 stimulates protein tyrosine kinases as well as at least two separate PI 3-kinase isoforms, namely the p85/p110 PI 3-kinase and PI3K-C2α. This is the first demonstration that MCP-1 can stimulate PI 3-kinase activation and is also the first indication of an agonist-induced activation of the PI3K-C2α enzyme. These two events may play important roles in MCP-1-stimulated signal transduction and biological consequences.
Schema Matching with Frequent Changes on Semi-Structured Input Files: A Machine Learning Approach on Biological Product Data

For small to medium sized enterprises matching schemas is still a time consuming manual task. Even expensive commercial solutions perform poorly, if the context is not suitable for the product. In this paper, we provide an approach based on concept name learning from known transformations to discover correspondences between two schemas. We solve schema matching as a classification task. Additionally, we provide a named entity recognition approach to analyze, how the classification task relates to named entity recognition. Benchmarking against other machine learning models shows that when choosing a good learning model, schema matching based on concept name similarity can outperform other approaches and complex algorithms in terms of precision and F1-measure. Hence, our approach is able to build the foundation for improved automation of complex data integration applications for small to medium sized enterprises.
GOAL: An eHealth Application for Rewarding Healthy Behaviour. The First Experiences of Older Adults

It is important to motivate older adults to lead an active lifestyle. Mobile games and applications can motivate older adults to increase physical, social and cognitive activity. Such a mobile application platform is developed within the GOAL project (H2020 project no. 731656). The aim of this paper is to introduce GOAL, to provide an overview of ongoing evaluations and to present the intermediate results concerning the usability of end-user acceptance of this technology among older adults. GOAL consists of three core components: the GOAL Website, the GOAL Web Portal and the GOAL Mobile Applications. The evaluation has an iterative approach. In total, there are four evaluation phases. During the first three phases, subjects are invited for a pre-test and post-test assessment. During phase 4, subjects are invited to use GOAL by sending out a mass email to get people to sign up to GOAL for themselves. The results of phase 1, 2 and 3 of the evaluation (especially the suggestions by the t arget group) lead to several points of improvement for the GOAL system, to increase the adoption of this technology by older adults. Over the phases, the level of usability increased and subjects become more and more enthusiastic about GOAL.
The failure index of hard rock under high geostress conditions

Yield and failure are the important content of materials' mechanical property and two different stages in the deformation process of materials which can be gained only by experiment. Based on systematic mechanical property experiment, especially true triaxial experimental which can simulate to the complicated evolvement of stress load which produced by excavating and support underground engineering under high stress conditions, a new Three Shear yield criteria is proposed for hard rock. According to the distance between a point in stress space and yield plan and the plastic deformation ability of materials, the failure index is established which can be used to tensile brittle failure, shear brittle failure and shear ductile failure. The limit value of effective plastic strain is proposed according to the experiment results. The research result can be used in the safety and stability analysis of hard rock underground engineering under high geo-stress conditions.
Analytics Applied to the Study of Reputational Risk through the Analysis of Social Networks (Twitter) for the El Dorado Airport in the City of Bogotá (Colombia)

Within a society increasingly technological and immersed in the different possibilities offered by the Internet as a channel of interaction and communication, there is a need for private and state entities to explore the information contained in social networks, an opportunity that implies new challenges associated with emerging risks such as reputational. For this reason, through the study of social networks, reputational risk and computational linguistics, an analysis of the strategic accounts of the El Dorado Airport of the city of Bogotá (Colombia) on Twitter (@bog_eldorado) is carried out, taking into account the mentions and most relevant hashtags, to identify the polarity through of BoW (Bag of Words) of your comments and infer the user experience, customer satisfaction and how this influences reputational risk.
"Attitude"- mHealth Apps and Users' Insights: An Empirical Approach to Understand the Antecedents of Attitudes towards mHealth Applications

Mobile health apps are a solution to make people more self-sufficient with their health in relation to their lifestyle or medical needs. The mobile health landscape is growing, but still its usage rate is rather low. Since the attitude towards mhealth apps has not been extensively studied so far, we conducted a multi-method approach study in Germany. To understand people’s needs, preferences and attitudes towards mhealth applications on the one hand and the antecedents of people’s attitudes towards mhealth applications on the other hand, we chose a user-centered approach. To get first insights into users’ needs and attitudes, we conducted two focus groups (N=11). Topics such as privacy concerns, data sharing and app functions have emerged as key issues. Quantitatively, we operationalized the collected key topics as well as users’ attributes and personal health attitudes. We collected 132 complete responses from originally 180 responses. We used partially-least squares structural equation modeling to identify the antecedents of attitudes towards mhealth applications. Results indicate that time saving and daily life facilitating functions are favored app features. Further, we found advantages (e.g. drink reminder) and disadvantages (e.g. data collection). Gender and age effects could be detected regarding health literacy, privacy concerns, and data sharing. Older participants are more cautious and reserved with the usage of health apps. Women in general show a higher health-literacy. eHealth literacy and intention to share data were identified as being the strongest influence on a positive attitude towards mhealth apps. To ensure that mhealth apps further enjoy a rising popularity, it is important to meet the users’ concerns about privacy and help them to acquire eHealth literacy.
Early Classification of Time Series by Hidden Markov Models with Set-Valued Parameters

Hidden Markov models are popular tools for the modeling of multivariate time series. A set-valued quantification of the parameters might increase realism in the description of non-stationarity. A recent work shows that the computation of the bounds of the likelihood of a sequence with respect to such imprecise quantification can be performed in the same polynomial time of a precise computation with sharp values. This is the basis for a credal classifier of time series. For each training sequence we learn a set-valued model and compute the interval likelihood of the test sequence. The returned classes are those of the models associated to the undominated intervals. The approach is particularly accurate on the instances for which single classes are returned. We therefore apply this method to early classification of streaming data. As soon as the credal classifier returns a single output we assign a class label even if the stream is not terminated. Tests on a speech recognition benchmark suggest that the proposed approach might outperform a thresholding of the precise likelihoods with the classical models.
Categories of Research Methods and Types of Research Results Illustrated with a Continuous Project

Research projects are often continuous, they are initiated by one researcher and continued by another. Each researcher needs to understand the continuous project and his part in it. This part we call a mini project. Explanation of research methods and projects results in literature does not help in understanding continuous research projects. Descriptions of research methods are often full of details and do not show relations between projects supported by different research methods. In this paper, we put all research methods into three categories and define two types of results. We suggest defining a mini project by a research method category and its result type. From such mini projects we build continuous projects. This way of structuring is illustrated with a continuous project investigating a view on changes of enterprise architecture. The relations of mini projects of different categories are generalized in a guidance for researchers, supervisors and reviewers.
Principles of Organizational Co-Evolution of Business and IT: a Complexity Perspective

The Business and IT Co-evolution (BITC) is a growing concern for researchers and practitioners alike. Extant literature on implementation and management of BITC is still in infancy and lacks especially empirical guidelines. This paper makes two contributions to the study of BITC. First, we summarize and systematically organize 10 BITC principles from prior literature to guide management efforts. Second, we build a system dynamics model based on the 10 principles to apply these principles as a means to improve the BITC management. The model embraces the emergent behaviors driven by the interactions of business and IT, and guides the BITC governance shaped by the principles. The development of this model forms a necessary step towards suggesting guidance how to implement BITC in companies. The paper also shows the capability of a system dynamic method to capture some of the holistic behaviors that emerge from implementing the 10 principles.
Rough Logs: A Data Reduction Approach for Log Files

Modern scalable information systems produce a constant stream of log records to describe their activities and current state. This data is increasingly used for online anomaly analysis, so that dependability problems such as security incidents can be detected while the system is running. Due to the constant scaling of many such systems, the amount of processed log data is a significant aspect to be considered in the choice of any anomaly detection approach. We therefore present a new idea for log data reduction called ‘rough logs’. It utilizes rough set theory for reducing the number of attributes being collected in log data for representing events in the system. We tested the approach in a large case study - the experiments showed that data reduction possibilities proposed by our approach remain valid even when the log information is modified due to anomalies happening in the system.
A Natural Language-Inspired Multi-label Video Streaming Traffic Classification Method Based on Deep Neural Networks

This paper presents a deep-learning based traffic classification method for identifying multiple streaming video sources at the same time within an encrypted tunnel. The work defines a novel feature inspired by Natural Language Processing (NLP) that allows existing NLP techniques to help the traffic classification. The feature extraction method is described, and a large dataset containing video streaming and web traffic is created to verify its effectiveness. Results are obtained by applying several NLP methods to show that the proposed method performs well on both binary and multilabel traffic classification problems. We also show the ability to achieve zero-shot learning with the proposed method.
Expression of High Mobility Group A2 Protein in Retinoblastoma and its Association With Clinicopathologic Features

Retinoblastoma (RB) is the commonest primary intraocular tumor in children. Overexpression of the high mobility group (HMG) A2 protein has been observed in a variety of malignant tumors and often correlates with poor prognosis. We studied the expression of HMGA2 in primary tumor samples and correlated with clinicopathologic features such as invasion, differentiation, and laterality of the tumors. Among 64 tumors, there were 29 tumors with invasion of the optic nerve, choroid, and/or orbit and 35 tumors without invasion. HMGA2 immunoreactivity was evaluated on archival paraffin sections and the results confirmed by Western blotting on 12 fresh tumor samples. Among 29 tumors with invasion, HMGA2 was strongly positive (++) in 10 tumors, moderately positive (+) in 11 tumors. Among 35 tumors without invasion, HMGA2 was strongly positive (++) in 6 tumors, moderately positive (+) in 6 tumors. Tumors with invasion showed significantly higher expression of HMGA2 compared with tumors without invasion (P<0.01). Non-neoplastic retina was negative for HMGA2. There was no correlation between HMGA2 expression and differentiation/laterality. Western blotting revealed that 7 tumors were strongly positive, 2 were moderately positive, and 1 was faintly positive for HMGA2. Our study has demonstrated the HMGA2 expression in a large cohort of primary retinoblastoma tumors and its correlation with invasiveness.
Assessing Finite-Control-Set Model Predictive Control: A Comparison with a Linear Current Controller in Two-Level Voltage Source Inverters

Model predictive control (MPC) methods for applications in power converters have received considerable attention in recent years. The idea behind MPC is to use a mathematical model of the system to predict its future behavior within a predefined time. An optimization problem that includes the control objectives, the predicted variables, and possible constraints of the system is solved, yielding the control actions to be applied.
Software Testing: Cost Reduction in Industry 4.0

Industry 4.0 is changing every perspective of production. This new industry requires the evolution of every aspect of the systems faster than ever before. In this situation, validation and verification emerge as a significant phase of the life cycle of software projects. A software failure could result in catastrophic consequences, so it is crucial that all systems work correctly in production. In this work, we present a collaborative project between the University of Cadiz and Navantia. Navantia is a leader shipbuilding Spanish company which develops grand industrial software projects. The objective of this work is to decrease the total cost of these projects by automatizing the software testing phase.