Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the intrinsic nature of the trends of digital innovation: A main path analysis

Digital innovation plays an increasingly important role in an organizational context. During the past two decades, IS scholars have witnessed a rapid growth of research interest in digital innovation. However, its nature and implications are still vague. More speciﬁcally, the intrinsic nature of digital innovation is changing with the development of technology and society. The purpose of this article is to examine digital innovation from various perspectives and thus provide potential research directions for future research. This study adopts main path analysis, a citation-based systematic review method, collecting and analyzing 848 digital innovation-related academic articles. It traces the most signiﬁcant paths and reveals seven popular research themes, including digital innovation management, recombination approach, entrepreneurship, transformation, institution and management control, and data-driven value capture. This study furthers the understanding of digital innovation in the current IS research and presents research opportunities that are valuable for future work.
The Effect of Training Set Timeframe on Future Performance of Machine Learning-based Malware Detection Models

The occurrence of previously unseen malicious code or malware is an implicit and ongoing issue for all software-based systems. It has been recognized that machine learning, applied to features statically extracted from binary executable files, offers a number of promising benefits, such as its ability to detect malware that has not been previously encountered. Nevertheless it is understood that these models will not continue to perform equally well over time as new and potentially less recognizable malwares occur. In this study, we have applied a range of machine learning models to the features extracted from a large collection of software executables in Portable Executable format ordered by the date the binary was first encountered, consisting of both malware and benign examples, whilst considering different training set configurations and timeframes. We analyze and quantify the relative performance deterioration of these machine learning models on future test sets of these features, and discuss some insights into the characteristics and rate of machine learning-based malware detection performance deterioration and training set selection.
Single Polymer Chain Surface Area as a Descriptor for Rapid Screening of Microporous Polymers for Gas Adsorption

The presented work shows that a qualitative assessment of the trends in surface area in a given microporous polymer family can be obtained by analyzing the properties of a single polymer chain. We analyzed the properties of polymers of intrinsic microporosity (PIMs) that contain imide groups (known as PIM-PIs) and obtained the surface area of available PIM-PIs and proposed some hypothetical structures that can result in higher surface areas and potentially more favourable properties for adsorption. The advantage of the proposed method is that it allows for a rapid screening of potential monomers avoiding the need to perform computationally expensive calculations.
Building Community Resilience Using Gain-Loss Framing to Nudge Homeowner Mitigation and Insurance Decision-making

Promoting homeowners’ preparation for natural disasters is a critical component of building community resilience. Adoption of protective actions by individual homeowners could reduce the risk of injury and damage to property; however, despite extensive public education programs, numerous studies report that households still are under-prepared for natural disasters. The effectiveness of gain-loss framing to nudge risk averse decision-making has been demonstrated across several domains, yet the application of gain-loss framing effects for natural disaster preparation has concentrated only on policy-level decisions. A behavioral experiment (N= 1,840) was conducted to test whether gain-loss framing can be used to nudge homeowner risk mitigation and insurance purchase decisions. Consistent with Prospect Theory, results indicate that a gain-frame is more likely to lead to risk averse decisions to mitigate for floods and hurricanes, but not for earthquakes. Disaster specific framing effects for nudging individual mitigation decisions provide unique implications building community resilience.
Applying Machine Learning to Study Infrastructure Anomalies in a Mid-size Data Center - Preliminary Considerations

Today, data centers deal with fast-growing data volumes. To deliver services, they deploy a growing amount of heterogeneous hardware. As a result, it becomes practically impossible to apply human-based data center management. For instance, in a real-world data center with 500+ computers – delivering data, computational, and network services, it becomes impossible to visualize and understand causal relationships among variables describing the performance of monitored resources. However, it is possible to collect data describing the behavior of individual nodes. Hence, such data may be used to analyze/model system performance. In particular, it may be applied to recognize and predict anomalies in system behavior. Furthermore, collected data should allow ﬁnding the cause(s) of anomalies. Therefore, “data-driven approaches” have been applied to the real-world data, to ﬁnd Root Cause of anomalies.
Usage Space Sampling for Fringe Customer Identification

With large numbers of available customers, it is often essential to select representative samples for reasons of computational cost reduction and upstream advanced data analytics. However, for many analytical procedures, the usage behavior observed from a smaller sample of customers must indicate well the fringe of usage and its relation to extreme product loads. Due to the high complexity of technical or service systems, it remains challenging to minimize the number of samples while sufﬁciently capturing the fringe customers. With the availability of data related to usage behavior, we consider a sampling method to address this problem by analyzing the customer usage space before sampling, then separately sampling fringe and core customers, and weighting the samples afterwards. Experimental results show that the method can identify fringe customers with signiﬁcantly fewer, yet reproducible samples, while maintaining the distribution representativeness of customer population to a large extend.
Beyond the Hype: Why Do Data-Driven Projects Fail?

Despite substantial investments, data science has failed to deliver significant business value in many companies. So far, the reasons for this problem have not been explored systematically. This study tries to find possible explanations for this shortcoming and analyses the specific challenges in data-driven projects. To identify the reasons that make data-driven projects fall short of expectations, multiple rounds of qualitative semi-structured interviews with domain experts with different roles in data-driven projects were carried out. This was followed by a questionnaire surveying 112 experts with experience in data projects from eleven industries. Our results show that the main reasons for failure in data-driven projects are (1) the lack of understanding of the business context and user needs, (2) low data quality, and (3) data access problems. It is interesting, that 54% of respondents see a conceptual gap between business strategies and the implementation of analytics solutions. Based on our results, we give recommendations for how to overcome this conceptual distance and carrying out data-driven projects more successfully in the future.
All About the Name: Assigning Demographically Appropriate Names to Data-Driven Entities

We develop a method for assigning demographically appropriate names to data-driven entities, such as personas, chatbots, and virtual agents. The value of this method is removing the time-consuming human effort in this task. To demonstrate our method, we collect four million user profiles with gender, age, and country information from an international online social network. From this dataset, we obtain 1, 031, 667 unique names covering 3, 088 demographic group combinations that our method considers as gender, age, and nationality appropriate. A manual evaluation by raters from 34 countries shows a demographic appropriateness score of 85.6%. The demographically appropriate names can be utilized for data-driven personas, virtual agents, chatbots, and other humanized entities.
Outstanding Employees Performance: Personality Traits, Innovation and Knowledge ‎Management

This paper analyzes the relationship among outstanding employees’ personality traits, innovation and performance. It stems from Motowidlo, Borman, & Schmit's theory of performance, which distinguishes between task and contextual performance, leading to personal job success and creativity. The innovative paired sample is composed of both, 189 outstanding employees and their supervisors, and a common employee (182) control group. Findings show that agreeableness and extraversion are significantly correlated to contextual behavior. The present paper contribution is that it enlightens for the first time the relationship the above-mentioned performance. This in turn can be employed as an assessment tool which can assist Human Resources Units in obtaining strategic knowledge of its employees for proactive management of their innovative knowledge assets, for better systematic management of organizational knowledge.
Designing an IT Risk Management Ontology grounded on Systematic Literature Review

Organizations that operate digital-based services rely heavily on Information Technology (IT). Nonetheless, this IT dependency inducts risks that could impact the achievement of organizations goals and even its own survival. One usual solution is to enforce an IT Risk Management (RM) approach to cope with IT-related risks. However, due to IT RM complexity and diversity, many organizations are not able to implement it successfully. Therefore, an IT RM ontology capturing the essential of IT RM concepts and its relations constitute a positive step towards the simplification and clarification of IT RM, which by its turn facilitates the IT RM enforcement. This paper designs an IT RM ontology, using DEMO, that is grounded in a SLR that follows the Kitchenham (2004) guidelines. The objective is to prescribe what key concepts, relationships and processes should be enforced to reduce the IT RM implementation effort when compared with an implementation from scratch.
Inferring Supplier Quality in the Gig Economy: The Effectiveness of Signals in Freelance Job Markets

Inferring quality of labor suppliers is a challenge in the gig economy. Many online freelance job markets address this challenge by incorporating signals. We test effectiveness of two kinds of information signals as indicators of supplier quality: skill signal (which reflects suppliers’ skill and potential), and achievement signal (which reflects suppliers’ past achievement). We theorize that two job characteristics in cross-national labor demand settings strengthen effectiveness of these signals: job duration, and cultural distance. Econometric analysis on a dataset from a leading online freelance job marketplace containing information on jobs posted by buyers and completed by suppliers located across several countries supports our hypotheses. We find that both skill and achievement signals are more effective at inferring supplier quality in jobs involving longer duration, and in jobs involving greater cultural distance between buyers and suppliers.
Memory cell with photoacoustic switching

The manufacturing of nanoscale devices with sizes smaller than 100 nm is founded on the quantum physical phenomena. We proposed the new nanoscale device with photoacoustic switching. The memory cell can be made by means of two thin silicon surface layers one of which contains oxygen in Si-O-Si bonding. The charge storage is caused by inserting on clean silicon layer the oxygen incorporated with silicon. The mechanical deformation of upper oxidized silicon layer results in shift of atomic positions. The oxygen appearance on the silicon surface is reflected on electronic structure as new defect level inside band gap. The electrons are stored on this oxygen related level with energy position Ec-0.18 eV. By applied bias voltage we realize the erase procedure by removing the stored electron. The silicon surface should be prepared because the oxygen incorporation depends on the chemical properties. The electronic structure of oxidized silicon surfaces with (111) and (100) orientation was tested by using second harmonic generation response. The characteristic time of storage, 1 ns, was measured by using the laser time-resolved short pulse spectroscopy. We used modelocked mode of laser system with pulse duration 120 ps. The speed of switching was approximately 1013 Hz.
Online-Computer-Mediated Interviews and Observations: Overcoming Challenges and Establishing Best Practices in a Human-AI Teaming Context

Prior research has established the feasibility of conducting online interviews and observations, yet there is limited guidance in how to interact with participants when conducting fully mediated research with screen-sharing and video. This study, conducted during early phases of COVID-19, included 15 volunteer tweet-annotators working with an emergency response organization. This method contribution uses cues-related and surveillance theories to reveal challenges and best practices when asking research participants to share their screen, be on video, and participate in a multiple-interview study. The findings suggest that researchers conducting online-mediated research should be prepared to provide technical support for the devices and interfaces participants use during the study, find ways to “see” beyond what is on the mediated screen, and consider ethical issues not often discussed. In addition to these findings, an output of this research is two brief training videos useful for other researchers embarking on conducting fully mediated research. © 2021 IEEE Computer Society. All rights reserved.
Rankings or Absolute Feedback? Investigating Two Feedback Alternatives for Negotiation Agreements in a Gamified Electronic Negotiation Training

The use of game elements in non-game contexts has gained popularity in the education domain to increase students’ motivation and engagement. Additionally, these elements provide feedback on students ’ performance. Rankings are often applied to display performance feedback relative to others despite their potential negative effects, for example due to increased pressure. In this experimental study, we compare two types of gamified electronic negotiation training, each including the game elements levels, badges, and experience points. As the reflection on the negotiation performance is a central activity for negotiation training, we test two feedback alternatives for the negotiation agreements. One group received relative feedback through rankings, and the other group received a non-game and absolute feedback called Pareto graph. Our findings show similar intrinsic motivation and negotiation outcomes, but higher engagement for participants using the Pareto graph. Practitioners and researchers are encouraged to consider non-game feedback elements in their gamification design.
The Challenges of Knowledge Combination in ML-based Crowdsourcing - The ODF Killer Shrimp Challenge using ML and Kaggle

Organizations are increasingly using digital technologies, such as crowdsourcing platforms and machine learning, to tackle innovation challenges. These technologies often require the combination of heterogeneous technical and domain-specific knowledge from diverse actors to achieve the organization’s innovation goals. While research has focused on knowledge combination for relatively simple tasks on crowdsourcing platforms and within ML-based innovation, we know little about how knowledge is combined in emerging innovation approaches incorporating ML and crowdsourcing to solve domain-specific innovation challenges. Thus, this paper investigates the following: What are the challenges to knowledge combination in domain-specific ML-based crowdsourcing? We conducted a case study of an environmental challenge – how to use ML to predict the spread of a marine invasive species, led by the Swedish consortium Ocean Data Factory Sweden using the crowdsourcing platform Kaggle. After discussing our results, we end the paper with recommendations on how to integrate crowdsourcing into domain-specific digital innovation processes.
Examining the Social Media Antecedents of Racial Justice: Evidence from Twitter

Despite an increasing body of research focusing on the use of social media posts for inferring various social phenomena such as influenza spread, political preferences, or information diffusion, less attention has been given to the influence such posts have on user attitudes. Rooted at the nexus of social capital, sociolinguistics, and the theory of critical consciousness, this study aims to explore the social media factors playing a role in racial justice attitude development. Using a matched sample of 5,365 Twitter users, we demonstrate the impact of user posts’ topical diversity and bridging and bonding capital on the likelihood of engaging in antiracist discourse online in the aftermath of George Floyd’s murder.
Location Privacy in the Era of 5G

—Fifth Generation (5G) wireless technology is usher-ing in a new age of interconnectivity, and as it does deﬁnitions of privacy may well change with it. In this paper we will focus on the changing nature of location privacy, while aspiring to increase community cognizance of vulnerabilities within the 5G network that threaten end-user privacy. To this end, we will address the statistical efﬁcacy of a multilateration attack utilizing timing advance commands within the 5G cloud radio access network, by showcasing that such an attack meets the Cram ´ er-Rao Lower Bound (CRLB) across each subcarrier spacing. We will also demonstrate how position estimates within 5G can be further reﬁned using methods previously shown to be effective in Long-Term Evolution (LTE) Networks. Lastly, we will demonstrate the attack in a simulated environment modeled after the conference grounds, using current real-world deployments of 5G on Kauai.
How To Transfer Tacit Knowledge for Living Lab Practice Consideration on Tacit Knowledge Representations

Despite strong attention and wide ranges of practice, living labs have hardly scaled up for a decade as expected. For living labs as one of the innovative methods for social challenges in cities with high complexity and uncertainty, one of the critical challenges is in knowledge transfer of its practice. Practical tacit knowledge for living lab is often embedded within process, organization and operation, and hardly externalized. The inexperienced practitioners face a lot of uncertainty in implementation of living lab without clues where to tackle. Aiming at promoting the living lab practice widely, this paper investigates tacit knowledge externalization with three different representations. The analysis and comparison of external representations indicate a strong compatibility pattern between representation styles and practitioners’ maturity level on the relevant field. In the living lab practice, how to convey tacti knowledge should be considered carefully, depending on practitioners’ maturity level.
Architectural Tactics for Energy Efficiency: Review of the Literature and Research Roadmap

The energy consequences of software are rapidly growing: at the high-end, server farms consume enormous amounts of energy; at the low-end there is ever-increasing reliance on battery-powered mobile and Internet-of-Things (IoT) devices. However, there has been little attention to how software architectures can be designed for energy efficiency. While other software qualities such as performance or availability have been extensively studied, there is little research on how to reason about energy-consumption as a first-class quality. We provide a basis for reasoning about design decisions for energy efficiency by deriving a set of reusable architectural tactics derived from the research literature, via a taxonomic literature review. We used an open-search and snowballing methodology to obtain primary studies, and then used thematic coding to identify commonalities among the design strategies described. The result of this process is a taxonomy of 10 architectural tactics for energy efficiency. These tactics provide a rational basis for architectural design and analysis for energy efficiency.
Supplementing Blood Diet With Plant Nectar Enhances Egg Fertility in Stomoxys calcitrans

Stomoxys calcitrans (stable fly) is a cosmopolitan biting fly of both medical and veterinary importance. Unlike blood-feeding-related behavior of stable fly, its plant feeding, the fitness value, and the S. calcitrans–plant interaction are less understood. Here we show based on two chloroplast DNA genes, ribulose bisphosphate carboxylase large chain (rbcL) and the intergenic spacer gene trnH-psbA, that field-collected male and female stable flies fed on various plant species. We investigated the fitness cost of plant feeding using Parthenium hysterophorus, one of the plant species identified to have been fed on by the field-collected flies. Supplementation of blood feeding with a flowering P. hysterophorus plant as nectar source enhanced egg hatchability significantly as compared to blood alone, showing the fitness value of nectar supplementation. However, nectar supplementation did not affect the number of eggs laid or longevity of S. calcitrans as compared to flies that fed on blood alone. S. calcitrans maintained on sugar alone failed to lay eggs. The various plants stable flies fed on demonstrated chemodiversity with their own signature scent. The behavioral response of S. calcitrans to these signature compounds varied from strong attraction (γ-terpinene) to neutral (linalool oxide and myrcene) to repellency (butanoic acid). Our study demonstrated that stable flies feed on nectar, and plant nectar supplementation of blood feeding enhanced larval emergence. Thus, our result has implication in stable fly reproduction, survival, disease transmission, boosting laboratory colony, and the possibility of using plant-derived odors for mass trapping of stable fly, for instance, using γ-terpinene.
Simulation Study on the Optimization of Photon Energy Delivered to the Prefrontal Cortex in Low-Level-Light Therapy Using Red to Near-Infrared Light

Brain functions have been proved to be affected by external stimuli. Low-Level-Light Therapy (LLLT) using near-infrared photons is one of the effective ways to modulate the hemodynamic activities in the brain. However, the biphasic hormetic dose-response where bioenergetics are stimulated at a low dose and inhibited at a high dose is well observed in all photon stimulations. The amount of photon energy delivered to the brain are affected by the wavelength as well as the multilayered head structure with variations of optical parameters (OPs). A real 3D volume head model is built for each participant in this study, and the boundary conditions of each OP in each layer is considered. The Monte Carlo simulation with wavelengths ranging from 650 nm to 1064 nm is implemented to investigate the energy delivered to the brain under different radiation profiles. Results show that 1064-nm photons penetrate deeper than 810-nm photons except for scalp absorption at the lower bound due to low melanin content. Collimated-beam radiation is better than diverging-beam due to a more uniform intensity distribution at the scalp surface. Further research to optimize LLLT dosage for each individual is imperative due to the high inter-person variability in structure and OPs.