Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Driven Service Innovation: a Systematic literature Review and Development of a Research Agenda

The potential created by ongoing developments in data and analytics permeates a multitude of research areas, such as the field of Service Innovation. In this paper, we conduct a Systematic Literature Review (SLR) to investigate the integration of data and analytics as an analytical unit into the field of Service Innovation – referred to as Data-Driven Service Innovation (DDSI). Overall, the SLR reveals three main research perspectives that span the research field of Data-Driven Service Innovation: Explorative DDSI, validative DDSI, and generative DDSI. This integrated theoretical framework describes the distinct operant roles of data analytics for Service Innovation, and thus contributes to the body of knowledge in the field of DDSI by providing three unified lenses, which researchers can use to describe and locate their existing and future research endeavors in this ample field. Building up on the insights from the SLR, a research agenda is proposed in order to trigger and guide further discussions and future research surrounding DDSI. Ultimately, this paper aims at contributing to the body of knowledge of Service Innovation in general and Data-Driven Service Innovation in particular by presenting a three-dimensional research space model structuring DDSI towards its advancement.
Can Digital Footprints Save the Physical Lecture?

We argue that better use of “digital footprints” (data generated from students’ learning activities) could be used to improve the traditional lecture. We point out some potentially important data sources, and briefly discuss how data from each of these sources can contribute to better learning. Finally, we argue that even if this development is possible, it will require changes – certainly changed priorities among faculty, but also probably recruitment of new types of expertise, for instance data scientists.
Incivility Detection in Online Comments

Incivility in public discourse has been a major concern in recent times as it can affect the quality and tenacity of the discourse negatively. In this paper, we present neural models that can learn to detect name-calling and vulgarity from a newspaper comment section. We show that in contrast to prior work on detecting toxic language, fine-grained incivilities like namecalling cannot be accurately detected by simple models like logistic regression. We apply the models trained on the newspaper comments data to detect uncivil comments in a Russian troll dataset, and find that despite the change of domain, the model makes accurate predictions.
Age-appropriate Participatory Design of a Storytelling Voice Input in the Context of Historytelling

With the demographic change, the percentage of older adults steadily increases. At the same time, new information and communication technologies (ICT) emerge at an ever-increasing rate, making it imperative to consider older adults in the development process to achieve the best possible usability and acceptance for older adults. This paper describes the development of a storytelling input component in the context of Historytelling (HT), which provides a digital interactive platform for older adults to share life stories across generations, potentially improving their health and wellbeing. HT follows the HCD+ (Human Centered Design for Aging) approach, claiming that older adults should be integrated as co-designers throughout the development process. A total of 19 older adults (M=68 years old) participated in 3 studies to analyze, evaluate and design a storytelling voice input, investigating voice communication technology for conversational agents. They were successfully involved in the design process, with methods adjusted to accommodate specific user characteristics of older adults and substantially contributed to the further development of the HT project, exploring the two central research questions regarding the type of voice input suitable for older adults and the minimal requirements for a conversational agent.
Quantitative Overfitting Management for Human-in-the-loop ML Application Development with ease.ml/meter

Simplifying machine learning (ML) application development, including distributed computation, programming interface, resource management, model selection, etc, has attracted intensive interests recently. These research efforts have significantly improved the efficiency and the degree of automation of developing ML models. In this paper, we take a first step in an orthogonal direction towards automated quality management for human-in-the-loop ML application development. We build ease. ml/meter, a system that can automatically detect and measure the degree of overfitting during the whole lifecycle of ML application development. ease. ml/meter returns overfitting signals with strong probabilistic guarantees, based on which developers can take appropriate actions. In particular, ease. ml/meter provides principled guidelines to simple yet nontrivial questions regarding desired validation and test data sizes, which are among commonest questions raised by developers. The fact that ML application development is typically a continuous procedure further worsens the situation: The validation and test data sets can lose their statistical power quickly due to multiple accesses, especially in the presence of adaptive analysis. ease. ml/meter addresses these challenges by leveraging a collection of novel techniques and optimizations, resulting in practically tractable data sizes without compromising the probabilistic guarantees. We present the design and implementation details of ease. ml/meter, as well as detailed theoretical analysis and empirical evaluation of its effectiveness.
Towards an Infrastructure for Empirical Research into Software Architecture: Challenges and Directions

While the software engineering community at large has embraced empirical studies of source code, empirical studies of software architecture are lacking. In order to increase the possibility and relevance of studies into software architecture, an infrastructure for sharing empirical data on software architecture is essential. This paper contributes by discussing needs and challenges in empirical studies of software architecture. Based on lessons learned we propose CoSARI - a community-wide infrastructure for empirical research into software architecture.
Data Governance and Information Governance: Set of Definitions in Relation to Data and Information as Part of DIKW

Chaos emerges with the ever growing amounts of data and information within organisations. But it is problematic to manage these valuable assets and also remain accountable and compliant for them because there is no agreement about even their definitions. Our objective is to propose a coherent set of definitions for data governance and information governance within and across organisations in relation with data and information as underlying concepts. As a research method, we explore elements from existing definitions in literature about the Data-Information-Knowledge-Wisdom pyramid and about data governance and information governance. Classification of these elements and coding them in concepts during discussions among peers resulted in a new vocabulary. This forms the basis for formulation and design of an original coherent set of definitions for data, information, meaning, data governance and information governance. This research is grounded, goal oriented and uses multiple accepted literature review methods. But it is limited to the literature found and the IS domain.
Maternal ancestry analyses of red tilapia strains based on D-loop sequences of seven tilapia populations

Background Many tilapia species or varieties have been widely introduced and have become an economically important food fish in China. Information on the genetic backgrounds of these populations is deficient and requires more research, especially for red tilapia strains. Methods In the present study, displacement loop (D-loop) sequences were used to evaluate the genetic relationship and diversity of seven tilapia populations that are widely cultured in China; this was done specifically to speculate on the maternal ancestry of red tilapia strains. Three red tilapia varieties of Oreochromis ssp. , Taiwan (TW), Israel (IL), and Malaysia (MY) strains and other populations, including O. aureus (AR), O. niloticus (NL), O. mossambicus (MS), and the GIFT strain of O. niloticus, were collected and analyzed in this study. Results A total of 146 polymorphic sites and 32 haplotypes of D-loop sequences were detected among 332 fish and four major haplotypes were shared among the populations. The TW and NL populations had a greater number of haplotypes (20 and 8, respectively). The haplotype diversity (Hd) and nucleotide diversity (π) of each population ranged from 0.234 to 0.826, and 0 to 0.060, respectively. The significant positive Tajima’s D value of neutral test were detected in the NL, IL, and MY populations (P < 0.05), which indicated these populations might have not experienced historical expansion. According to the pairwise F-statistics, highly significant genetic differentiations were detected among populations (P < 0.01), with the exception of the IL and MY populations (P > 0.05). The nearest K2P genetic distance (D = 0.014) was detected between the MS and TW populations, whereas, the farthest (D = 0.101) was found between the GIFT and AR populations. The results from the molecular variance analysis (AMOVA) showed that there was an extremely significant genetic variation observed among the populations (P < 0.01), which contained 63.57% of the total variation. In view of the genetic relationship of red tilapia strains with other populations, TW and IL were detected with more similar genetic structures related to MS, and MY was more genetically similar to GIFT (or NL), which could provide more genetic evidence for the red tilapia strains maternal ancestry.
A Corpus of Negations and their Underlying Positive Interpretations

Negation often conveys implicit positive meaning. In this paper, we present a corpus of negations and their underlying positive interpretations. We work with negations from Simple Wikipedia, automatically generate potential positive interpretations, and then collect manual annotations that effectively rewrite the negation in positive terms. This procedure yields positive interpretations for approximately 77% of negations, and the final corpus includes over 5,700 negations and over 5,900 positive interpretations. We also present baseline results using seq2seq neural models.
GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling

Current state-of-the-art systems for sequence labeling are typically based on the family of Recurrent Neural Networks (RNNs). However, the shallow connections between consecutive hidden states of RNNs and insufficient modeling of global information restrict the potential performance of those models. In this paper, we try to address these issues, and thus propose a Global Context enhanced Deep Transition architecture for sequence labeling named GCDT. We deepen the state transition path at each position in a sentence, and further assign every token with a global representation learned from the entire sentence. Experiments on two standard sequence labeling tasks show that, given only training data and the ubiquitous word embeddings (Glove), our GCDT achieves 91.96 F1 on the CoNLL03 NER task and 95.43 F1 on the CoNLL2000 Chunking task, which outperforms the best reported results under the same settings. Furthermore, by leveraging BERT as an additional resource, we establish new state-of-the-art results with 93.47 F1 on NER and 97.30 F1 on Chunking.
Using quantitative real-time PCR to determine the hygienic status of cattle manure.

We developed a suitable system of DNA extraction and real-time quantitative polymerase chain reaction (qPCR) for the specific and sensitive quantification of pathogens and other relevant (indicator) organisms in recalcitrant material such as cattle manure. PCR inhibition by coextraction of humic compounds was minimized in this system, resulting in detection sensitivity of one target DNA copy per reaction well. Data from qPCR analysis for Escherichia coli agreed with cultivation based results, but orders of magnitude more fecal enterococci, Enterobacteriaceae and Campylobacter jejuni, were determined by qPCR than by cultivation. These bacteria may have been in a potentially hazardous active but non-cultivable state. The qPCR system is much less time consuming than conventional cultivation, highly specific, can detect non-cultivable organisms, provides high measurement throughput, and is cost attractive. It should be considered as an alternative in various application areas for (prescribed routine) cultivation based assays, e.g. for biosafety and hygiene monitoring.
Data-Driven Business Models - Building the Bridge between Data and Value

In the wake of an ongoing digital transformation organizations are seeking to understand and generate new data-driven business models. While data is a key resource, generating value from data is a key challenge for innovating data-driven business models. Extant tools for business model representation offer little help. In this paper, we propose the design of a Data Insight Generator (DIG) artefact that can support the design process of data-driven business models at a crucial step: connecting data to value propositions. The artefact is positioned as a complement to the Business Model Canvas, the most widely used tool for business model representation. The DIG connects two key elements of the business model, namely key resources and value proposition through six dataspecific elements. Further, the DIG supports an iterative process of discovery for these elements as a boundary object between business and data science/IT participants of business model innovation. Based on a formative evaluation we demonstrate the usability and utility of the DIG.
Stem cell culture and differentiation in microfluidic devices toward organ-on-a-chip

Microfluidic lab-on-a-chip provides a new platform with unique advantages to mimic complex physiological microenvironments in vivo and has been increasingly exploited to stem cell research. In this review, we highlight recent advances of microfluidic devices for stem cell culture and differentiation toward the development of organ-on-a-chip, especially with an emphasis on vital innovations within the last 2 years. Various aspects for improving on-chip stem-cell culture and differentiation, particularly toward organ-on-a-chip, are discussed, along with microenvironment control, surface modification, extracellular scaffolds, high throughput and stimuli. The combination of microfluidic technologies and stem cells hold great potential toward versatile systems of ‘organ-on-a-chip’ as desired. Adapted with permission from [1–8].
Driving NPD Performance in High-Tech SMEs through IT Ambidexterity: Unveiling the Influence of Leadership Decision-Making Styles

The scarcity of IT resources and technological advancements in high-tech small and medium enterprises (SMEs) require leaders to embed IT ambidexterity – simultaneous pursuit of IT exploitation and IT exploration activities – into their organization’s strategy, which could be challenging. To better understand how leaders enable IT ambidexterity, this study focuses on the leadership decision-making style (directive decision-making and participative decision-making) as a key driving factor. Moreover, we examine how and when leadership decision-making styles are most effective in enacting IT ambidexterity by considering new product development (NPD) team diversity and shared vision as two important contingencies. Finally, we test the role of IT ambidexterity in improving NPD performance. We analyse our research model using survey data from 292 high-tech SMEs. Our findings suggest that both decision-making styles enable IT ambidexterity, however, participative decision-making style is more effective in highly diverse NPD teams and directive decision-making style should be preferred when shared vision is a dominant factor among NPD team members. Our results also show that IT ambidexterity significantly enhances NPD performance. We discuss our contribution to information systems (IS) and ambidexterity research and provide implications for practice.
Scaling behavior in thermoelectric misfit cobalt oxides.

We investigate both thermoelectric and thermodynamic properties of the misfit cobalt oxide [Bi1.7Co0. 3Ca2O4]RS0.6CoO2. A large negative magnetothermopower is found to scale with both magnetic field and temperature, revealing a significant spin entropy contribution to thermoelectric properties giving rise to a constant S0 approximately 60 microV K-1. Specific heat measurements allow us to determine an enhanced electronic part with gamma approximately 50 mJ (mol K2)-1 attesting to strong correlations. Thereby, the comparison between cobaltites and other materials reveals a universal behavior of the thermopower slope as a function of gamma, testifying to a purely electronic origin. This potentially generic scaling behavior suggests here that the high room temperature value of the thermopower in misfit cobalt oxides results from the addition of a spin entropy contribution to an enlarged electronic one.
DECAY AND MOLD RESISTANCE OF BORATE MODIFIED ORIENTED STRANDBOARD

Decay and mold resistance of zinc borate (ZB) and calcium borate (CB) modified oriented strandboard (OSB) from southern mixed hardwoods and yellow pine was investigated in this study. Tests were done with brown rot, Gloeophyllum trabeum, and white-rot, Trametes versicolar, fungi for 8 and 12 weeks, respectively. Wood species and fungus type had significant influence on the decay resistance. Decay caused by brown rot was evident for all untreated southern pine and mixed hardwood OSB controls. White rot, however, did not cause significant sample weight loss for both species groups. The incorporation of ZB and CB into the OSB provides suitable protection against brownand white-rot fungi. No significant weight loss was observed from the treated OSB. Microscopic analysis shows that the hyphae were abundant in wood rays and cell walls in the southern pine OSB controls attacked by G. trabeum. Untreated samples from mixed hardwoods and commercial OSB were most susceptible to mold growth. The borate-modified OSB from mixed hardwoods and southern pine effectively prevented the mold growth. Mold resistance was achieved with increase of borate retention level in the samples. Mixed hardwoods OSB showed a higher susceptibility to mold growth than southern pine OSB with or without borate treatment.
Mining of High Dimensional Data using Efficient Feature Subset Selection Clustering Algorithm (WEKA)

We exhibited the thought of data mining through the free and open source programming Waikato Environment for Knowledge Analysis (WEKA), which allows you to burrow own data for examples and cases. We moreover depicted about the first methodology for data mining — backslide — which allows you to anticipate a numerical worth for a given set of insight qualities. This method for dismemberment is most easy to perform and the base fit system for data mining, yet it filled a not too bad need as a prolog to WEKA and gave a not too bad example of how unrefined data can be changed into convincing information. We will take you through two additional data mining techniques that are hardly more mind boggling than a backslide model, however all the more compelling in their individual goals. Where a backslide model could simply accommodate you a numerical yield with specific inputs, these additional models grant you to translate your data particularly data mining is about applying the right model to your data. You could have the best data about your customers (whatever that even means), however in case you don't have any kind of effect the right models to it, it will just be refuse. Consider this an exchange way: If you recently used backslide models, which make a numerical yield, how would Amazon have the ability to let you know "Distinctive Customers Who Bought X Also Bought Y? " There's no numerical limit that could accommodate you this kind of information. So we should delve into the two additional models you can use with your witness.
An Architectural Blueprint for a Multi-purpose Anomaly Detection on Data Streams

Anomaly detection means a hypernym for all kinds of applications finding unusual patterns or not expected behaviour like identifying process patterns, network intrusions or identifying utterances with different meanings in texts. Out of different algorithms artificial neuronal nets, and deep learning approaches in particular, tend to perform best in detecting such anomalies. A current drawback is the amount of data needed to train such net-based models. Moreover, data streams make situation even more complex, as streams cannot be directly fed into a neuronal net and the challenge to produce stable model quality remains due to the nature of data streams to be potentially infinite. In this setting of data streams and deep learning-based anomaly detection we propose an architecture and present how to implement essential components in order to process raw input data into high quality information in a constant manner.
Vehicle Routing Problem for Information Collection in Wireless Networks

Advances in computer network architecture add continuously new features to vehicle routing problems. In this work, the Wireless Transmission Vehicle Routing Problem (WT-VRP) is studied. It looks for a route to the vehicle responsible for collecting information from stations as well as an efficient information collection planning. The new feature added here is the possibility of picking up information via wireless transmission, without visiting physically the stations of the network. The WT-VRP has applications in underwater surveillance and environmental monitoring. We discuss three criteria for measuring the efficiency of a solution and propose a mixed integer linear programming formulation to solve the problem. Computational experiments were done to access the numerical complexity of the problem and to compare solutions under the three criteria proposed.
Sequence Coverage Directed Greybox Fuzzing

Existing directed fuzzers are not efficient enough. Directed symbolic-execution-based whitebox fuzzers, e.g. BugRedux, spend lots of time on heavyweight program analysis and constraints solving at runtime. Directed greybox fuzzers, such as AFLGo, perform well at runtime, but considerable calculation during instrumentation phase hinders the overall performance. In this paper, we propose Sequence-coverage Directed Fuzzing (SCDF), a lightweight directed fuzzing technique which explores towards the user-specified program statements efficiently. Given a set of target statement sequences of a program, SCDF aims to generate inputs that can reach the statements in each sequence in order and trigger bugs in the program. Moreover, we present a novel energy schedule algorithm, which adjusts on demand a seed's energy according to its ability of covering the given statement sequences calculated on demand. We implement the technique in a tool LOLLY in order to achieve efficiency both at instrumentation time and at runtime. Experiments on several real-world software projects demonstrate that LOLLY outperforms two well-established tools on efficiency and effectiveness, i.e., AFLGo–a directed greybox fuzzer and BugRedux–a directed symbolic-execution-based whitebox fuzzer.