Miracle-dz/UniFAR-FTUs huggingface dataset:
Biofilm Microbiome (Re)Growth Dynamics in Drinking Water Distribution Systems Are Impacted by Chlorine Concentration

Biofilms are the dominant form of microbial loading (and organic material) within drinking water distribution systems (DWDS), yet our understanding of DWDS microbiomes is focused on the more easily accessible bulk-water. Disinfectant residuals are commonly provided to manage planktonic microbial activity in DWDS to safeguard water quality and public health, yet the impacts on the biofilm microbiome are largely unknown. We report results from a full-scale DWDS facility used to develop biofilms naturally, under one of three chlorine concentrations: Low, Medium, or High. Increasing the chlorine concentration reduced the bacterial concentration within the biofilms but quantities of fungi were unaffected. The chlorine regime was influential in shaping the community structure and composition of both taxa. There were microbial members common to all biofilms but the abundance of these varied such that at the end of the Growth phase the communities from each regime were distinct. Alpha-, Beta-, and Gamma-proteobacteria were the most abundant bacterial classes; Sordariomycetes, Leotiomycetes, and Microbotryomycetes were the most abundant classes of fungi. Mechanical cleaning was shown to immediately reduce the bacterial and fungal concentrations, followed by a lag effect on the microbiome with continued decreases in quantity and ecological indices after cleaning. However, an established community remained, which recovered such that the microbial compositions at the end of the Re-growth and initial Growth phases were similar. Interestingly, the High-chlorine biofilms showed a significant elevation in bacterial concentrations at the end of the Re-growth (after cleaning) compared the initial Growth, unlike the other regimes. This suggests adaptation to a form a resilient biofilm with potentially equal or greater risks to water quality as the other regimes. Overall, this study provides critical insights into the interaction between chlorine and the microbiome of DWDS biofilms representative of real networks, implications are made for the operation and maintenance of DWDS disinfectant and cleaning strategies.
What promote the mobilization and coordination of knowledge for crowdsourced digital innovation?

This paper explores how an activity object serves to promote the mobilization and coordination of knowledge for spurring crowdsourced digital innovation. After reviewing the literature, I develop two hypotheses to investigate the role of an activity object in knowledge mobilization and knowledge coordination. By using a mixed-methods research approach, this study suggests that an activity object is able to promote the mobilization and coordination of knowledge for creating digital innovations through triggering expansive learning, directing and motivating crowdsourced communities. Hence, my paper makes one contribution: it identifies the independent, active role of an activity object as a trigger for expansive learning and, a director and motivator, contributing a new understanding of the interacting roles of material artefacts and humans in crowdsourced digital innovation.
On a Strategy of Advancement of TMS Based Methods for Studying NCC

The well-known advantages of transcranial magnetic stimulation (TMS) in studying the brain mechanisms of mental processes are its noninvasiveness and possibility to study causal effects in addition to the purely correlational ones. Recent years have been marked by numerous advancements in the methods where TMS is used (e.g., Romei et al., 2016). The powerfulness of TMS approaches is enhanced by combining TMS with EEG recording, allowing understand the nature of TMS effects and their relation to consciousness mechanisms better (e.g., Massimini et al., 2012; Sarasso et al., 2015; Vernet et al., 2015; Nieminen et al., 2016). However, there are also numerous shortcomings and underdevelopment in TMS based commonly adopted experimental paradigms. For example, the combined TMS/EEG research mostly focuses on markers of consciousness states and less so on the mechanisms of contents of consciousness of alert subjects. Moreover, we do not know precisely what exactly happens in neurons and brain tissue in the areas directly under the focus of TMS. This is despite some early promising steps in this direction (e.g., Allen et al., 2014; Murphy et al., 2015). The neurobiological artificially produced non-natural effect caused by a sufficiently strong pulse directly targeted at the area of interest quite likely differs from what would be the natural neural state in this area in response to afference arriving via pathways artificially non-perturbed. This suggests use TMS by evoking pathway activity from areas remote from the cortical locus of interest. There is also a technical limitation when we try to stimulate and record the same area. One promising TMS paradigm is relatively free of the two above noted limitations: (i) stimulation of areas remote from the neurons known to be the carriers of the contents of perception (ii) associated with TMS-evoked potential (TEP) measurement of the effects recorded by the electrodes placed far from the target of TMS (but close to the area of content encoding neurons); (iii) all this ought to be combined with the additional methods allowing to interpret these bioelectric signatures in a meaningful way. While scalp-recorded event-related potential (ERP) and TEP generation mechanisms in human brain continue to be difficult to interpret in terms of their underlying neuronal mechanisms, invasive animal studies can inform us better about these mechanims. In what follows an example of this strategy is suggested.
Behind Cyber Doors: Securing the Internet of Self

The role of citizens in securing their smart homes is critical. We develop a research model, based on healthrelated fear appeal frameworks, to examine the factors that motivate users to take security precautions. Our model synthesizes the protection motivation theory and the extended parallel process model. This synthesis distinguishes between users’ danger-control and fear-control coping mechanisms. Further, we draw from the criminology literature to refine fear’s compatibility with information threats as opposed to health ones. We also develop and incorporate the Internet of Self into our model to account for smart homes’ personal relevance to users. The theoretical foundation, contributions and hypotheses are discussed.
A 3D face registration algorithm based on conformal mapping

Recently, 3D facial datasets are more easily available, and at the same time, the research of 3D face becomes more and more important. One of the most important research fields is 3D face registration, which plays an important role in face recognition, face shape analysis, and face animation. However, one of the most challenging issues in 3D face registration is to obtain a unique mapping for faces with different expression and landmark constraints. In this paper, we propose a novel conformal mapping algorithm to deal with the 3D face registration. Besides, the calculation is about harmonic energy, which makes our method applicable to low‐quality meshes. We begin with a harmonic mapping, then minimize the harmonic energy by a specific boundary condition on surfaces, and to obtain the conformal mapping, finally, we use a landmark‐constrained surface registration algorithm to register faces. Numerical experiments on various surfaces demonstrate the efficiency and robustness of our method.
Toward the Language Oscillogenome

Language has been argued to arise, both ontogenetically and phylogenetically, from specific patterns of brain wiring. We argue that it can further be shown that core features of language processing emerge from particular phasal and cross-frequency coupling properties of neural oscillations; what has been referred to as the language ‘oscillome. ’ It is expected that basic aspects of the language oscillome result from genetic guidance, what we will here call the language ‘oscillogenome,’ for which we will put forward a list of candidate genes. We have considered genes for altered brain rhythmicity in conditions involving language deficits: autism spectrum disorders, schizophrenia, specific language impairment and dyslexia. These selected genes map on to aspects of brain function, particularly on to neurotransmitter function. We stress that caution should be adopted in the construction of any oscillogenome, given the range of potential roles particular localized frequency bands have in cognition. Our aim is to propose a set of genome-to-language linking hypotheses that, given testing, would grant explanatory power to brain rhythms with respect to language processing and evolution.
Immersive Virtual Environments in Corporate Education and Training

The use of immersive virtual environments (IVE) in several areas has increased significantly in recent years, mainly due to lower costs, higher quality and increased availability of virtual reality (VR) tools and technology. Especially in formal education, numerous studies have shown positive learning outcomes of IVE. There is great potential to apply IVE solutions in training and corporate education. For those engaged in innovative, cost-effective and ergonomic new solutions for the field of corporate training and education, it is extremely valuable to understand the state of the art. However, so far, no study has consolidated this information. Thus, given the importance of the theme, this study seeks to organize and synthesize the results of studies published in this area. A total of 3516 studies were gathered and 12 selected for in-depth analysis and discussion, with the goal of providing a comprehensive review of technology, applications and trends.
How to Implement Clan Control in DevOps Teams

Clan control is an important factor for project success. A clan is a group with strong social capital. Hence, the implementation of social capital is a precondition for clan control and project success. However, for managers it is still a challenge to build and motivate clan control within IT projects, because people have different skills and backgrounds. Especially in rapid application development projects when new project methods or approaches like agile software development or DevOps are applied. Thus, we build up on existing research and explore how clan control can be build and leverage within cross-functional DevOps teams. With the help of expert interviews with team leads and managers of different DevOps teams, we derived structural, cognitive and relational ties of social capital. Additionally, we present leverage factors from our data for social capital to facilitate clan control. Finally, we give implications how these factors can be implemented.
Trolls and Social Movement Participation: An Empirical Investigation

Pratyush Bharati University of Massachusetts, Boston Pratyush.Bharati@umb.edu Carol Lee University of Massachusetts, Boston Carol.Lee002@umb.edu Romilla Syed University of Massachusetts, Boston Romilla.Syed@umb.edu Abstract This research examines the impact of trolling behavior on individuals’ participation in social movements. Troll behavior that disrupts online discourse can mislead or influence users to stray away from the main thrust of the social movement. There have been very limited information systems studies on how trolls influence social ties in specific organizational and social contexts. Current troll research is limited to the technical aspects of identifying troll characteristics. We annotated 217,582 tweets on the Stand with Jawaharlal Nehru University (JNU) movement by following a data dictionary approach and generated a word frequency for all the tweets to manually identify troll words. Researchers independently analyzed the tweets to label them based on troll characteristics. Our preliminary analysis of the troll tweet discussions elucidates the dynamics of trolls’ social influence in a social movement.
The Generic Blockchain Ecosystem and its Strategic Implications

The emergence of blockchain technology, most known due to the hype around Bitcoin, has the potential to transform entire industries, such as banking, insurance, or the Internet of Things (IoT). Yet, parallel ecosystems like cryptocurrencies that substitute products and services of traditional financial institutions emerged. However, literature does not provide a structured overview of the blockchain ecosystem. By analyzing 479 blockchain companies reported in the Crunchbase database, this paper visualizes the current blockchain ecosystem using the e3-value method consisting of eleven generic roles. Moreover, we identify three strategic implications where blockchain is fundamentally different from prior approaches: governance, trust, and openness. Scholars can apply the generic ecosystem for future research, while practitioners can use the model to identify possible disruptive actors or potential business opportunities
biggy: An Implementation of Unified Framework for Big Data Management System

Various tools, softwares and systems are proposed and implemented to tackle the challenges in big data on different emphases, e.g., data analysis, data transaction, data query, data storage, data visualization, data privacy. In this paper, we propose datar, a new prospective and unified framework for Big Data Management System (BDMS) from the point of system architecture by leveraging ideas from mainstream computer structure. We introduce five key components of datar by reviewing the cur- rent status of BDMS. Datar features with configuration chain of pluggable engines, automatic dataflow on job pipelines, intelligent self-driving system management and interactive user interfaces. Moreover, we present biggy as an implementation of datar with manipulation details demonstrated by four running examples. Evaluations on efficiency and scalability are carried out to show the performance. Our work argues that the envisioned datar is a feasible solution to the unified framework of BDMS, which can manage big data pluggablly, automatically and intelligently with specific functionalities, where specific functionalities refer to input, storage, computation, control and output of big data.
Improving Annotation for 3D Pose Dataset of Fine-Grained Object Categories

Existing 3D pose datasets of object categories are limited to generic object types and lack of fine-grained information. In this work, we introduce a new large-scale dataset that consists of 409 fine-grained categories and 31,881 images with accurate 3D pose annotation. Specifically, we augment three existing fine-grained object recognition datasets (StanfordCars, CompCars and FGVC-Aircraft) by finding a specific 3D model for each sub-category from ShapeNet and manually annotating each 2D image by adjusting a full set of 7 continuous perspective parameters. Since the fine-grained shapes allow 3D models to better fit the images, we further improve the annotation quality by initializing from the human annotation and conducting local search of the pose parameters with the objective of maximizing the IoUs between the projected mask and the segmentation reference estimated from state-of-the-art deep Convolutional Neural Networks (CNNs). We provide full statistics of the annotations with qualitative and quantitative comparisons suggesting that our dataset can be a complementary source for studying 3D pose estimation. The dataset can be downloaded at this http URL
Abstractive Summarization Using Attentive Neural Techniques

In a world of proliferating data, the ability to rapidly summarize text is growing in importance. Automatic summarization of text can be thought of as a sequence to sequence problem. Another area of natural language processing that solves a sequence to sequence problem is machine translation, which is rapidly evolving due to the development of attention-based encoder-decoder networks. This work applies these modern techniques to abstractive summarization. We perform analysis on various attention mechanisms for summarization with the goal of developing an approach and architecture aimed at improving the state of the art. In particular, we modify and optimize a translation model with self-attention for generating abstractive sentence summaries. The effectiveness of this base model along with attention variants is compared and analyzed in the context of standardized evaluation sets and test metrics. However, we show that these metrics are limited in their ability to effectively score abstractive summaries, and propose a new approach based on the intuition that an abstractive model requires an abstractive evaluation.
Privacy by Design to Comply with GDPR: A Review on Third-Party Data Processors

As the General Data Protection Regulation (GDPR) within the European Union comes into effect, organizations need to cope with novel legal requirements regarding the processing of user data and particularly how other, in the service integrated, organizations can process these. Information systems (IS) and their design as mashing up services of various providers (ecosystems) is state of practice. The GDPR raises for companies the question of how they can ensure that operations conform with external data processors according to the regulation. The approach of Privacy by Design (PbD), which is also included in the GDPR, offers for organizations a way to operationalize these legal requirements. Therefore, we conduct the first, rigorous, and systematic literature review of PbD. Specifically, we focus on works that seek implementation of PbD in organizations, located in ecosystems. The results show a surprising dearth of research in this field, although GDPR explicitly emphasizes this critical issue.
ERP System Implementation and Sustainability Performance Rating and Reputation

As organizations strive to meet stakeholders’ needs for sustainability information, they convey their status as sustainability leaders by publishing reports and seeking sustainability awards. Our research examines whether the extent of Enterprise Resource Planning (ERP) system implementation, which enables data and process integration across multiple functional areas, increases organizations’ ability to capture and integrate information for sustainability reporting. We propose that ERP systems implementation increases organizations’ ability to capture sustainability data and provides more and more favorable disclosures, resulting in better sustainability assessment scores by rating agencies and indices such as MSCI and the Dow Jones Sustainability Index. We also pose research questions about the type of ERP modules that are implemented and how those impact the scores related to the social, environmental, and governance dimensions of sustainability leadership. Our study is expected to contribute to the sustainability reporting and reputation, and the ERP systems literature, and to practice.
Differences in Neurocognitive Mechanisms Underlying the Processing of Center-Embedded and Non–embedded Musical Structures

In music, chords are organized into hierarchical structures based on recursive or embedded syntax. How the brain extracts recursive grammar is a central question in musical cognition and other cognitive neuroscience, but the precise mechanism remains unclear. By analyzing event related potentials (ERPs) and neural oscillatory activity, the present study investigated neurocognitive mechanisms underlying the processing of center-embedded structure in music by examining the differences in center-embedded and non-embedded structure processing and evaluating how these differences are affected by musical proficiency. Based on Western musical proficiency, the subjects were divided into two groups, non-experts and experts. The results revealed that for non-experts, the processing of center-embedded structure elicited greater early right-anterior negativity (ERAN) and N5 components as well as, reduced alpha and gamma activities than did the non-embedded structure. For experts, no significant difference in the ERP response was observed between the processing of non-embedded and center-embedded structures; however, the processing of center-embedded structure elicited increased beta activity compared to non-embedded structure. These findings indicate that listeners different in proficiency would rely on different cognitive neural mechanisms in music processing with the syntactic complexity increases.
"Agile Meets Non-Agile": Implications of Adopting Agile Practices at Enterprises

The question of how to increase speed and flexibility in times of digital disruption is essential to virtually any company. While previous research focuses on agility as software development practice or as form for organizing start-ups, little knowledge exists about agility at enterprises and its implications. Agile practices are currently adopted at enterprises in large-scale. While first steps towards agility are made quickly, we observed that specific challenges emerge when enterprises adopt agile practices. Drawing on results of an explorative study with ten global companies and on socio-technical systems theory, we reveal that adopting agile practices has far-reaching implications on products, processes, technology, people, and structure. This study contributes to agility research with specific insights on enterprise agility and its implications. While enterprises need to increase speed and flexibility to master digital transformation, an early incorporation of the implications of adopting agile practices fosters its sustainable implementation.
Evaluating deep learning models for sentiment classification

Deep learning has emerged as an effective solution to various text mining problems such as document classification and clustering, document summarization, web mining, and sentiment analysis. In this paper, we describe our work on investigating several deep learning models for a binary sentiment classification problem. We used movie reviews in Turkish from the website www.beyazperde. com to train and test the deep learning models. We also report a detailed comparison of the models in terms of accuracy and time performances. Two major deep learning architectures used in this study are Convolutional Neural Networks and Long Short‐Term Memory. We built several variants of these models by changing the number of layers, tuning the hyper‐parameters, and combining models. Additionally, word embeddings were created by applying the word2vec algorithm with a skip‐gram model on a large dataset (∼ 13 M words) composed of movie reviews. We investigate the effect of using the pre‐word embeddings with these models. Experimental results have shown that the use of word embeddings with deep neural networks effectively yields performance improvements in terms of run time and accuracy.
Text Analytics for Employee Dissatisfaction in Human Resources Management

Paperwork often inundates human resources departments, suggesting value in analytics that identify the most pressing issues of employee dissatisfaction. This study assesses text analytics techniques for this problem by utilizing Indeed.com employee reviews. These techniques provide value to human resources management by rapidly prioritizing documents. We compare general sentiment analyses with dictionaries curated for this domain, and the domain-specific approaches offer superior prediction of employee dissatisfaction. These results demonstrate the capability of text analytics in human resources management and offer tools for professionals to prioritize documents in need of the most urgent review.