Reviewerly/exHarmony huggingface dataset:
Assessing Text Representation Methods on Tag Prediction Task for StackOverflow

A large part of knowledge evolves outside of the operations of an organization. Question and answer online social platforms provide an important source of information to explore the underlying communities. StackOverflow (SO) is one of the most popular question and answer platforms for developers, with more than 23 million questions asked. Organizing and categorizing data is crucial to manage knowledge in such large quantities. Questions posted on SO are assigned a set of tags and textual content of each question may contain coding syntax. In this paper, we evaluate the performance of multiple text representation methods in the task of predicting tags for SO questions and empirically prove the impact of code syntax in text representations. The SO dataset was sampled and questions without code syntax were identified. Two classical text representation methods consisting of BoW and TF-IDF were selected along four other methods based on pre-trained models including Fasttext, USE, Sentence-BERT and Sentence-RoBERTa. Multi-label k'th Nearest Neighbors classifier was used to learn and predict tags based on the similarities between feature-vector representations of the input data. Our results indicate a consistent superiority of the representations generated from Sentence-RoBERTa. Overall, the classifier achieved a 17% or higher improvement on F1 score when predicting tags for questions without any code syntax in content.
Synthetic APIs: Enabling Language Models to Act as Interlocutors Between Natural Language and Code

Large language models (LLMs) can synthesize code from natural language descriptions or by completing code in-context. In this paper, we consider the ability of LLMs to synthesize code, at inference time, for a novel API not in its training data, and specifically examine the impact of different API designs on this ability. We find that: 1) code examples in model training data seem to facilitate API use at inference time; 2) hallucination is the most common failure mode; and 3) the designs of both the novel API and the prompt affect performance. In light of these findings, we introduce the concept of a Synthetic API: an API designed to be used by LLMs instead of by humans. Synthetic APIs for LLMs offer the potential to further accelerate development of natural language interfaces to arbitrary tools and services.
Designing a Conversational AI Agent: Framework Combining Customer Experience Management, Personalization, and AI in Service Techniques

Conversational AI agents are fundamentally changing how firms are delivering service to their customers. The rapid advancement of technology and ready tools means that deploying a conversational AI agent has become far simpler than ever imagined. However, customers remain unsatisfied with their experience and firms are unable to demonstrate value of conversational AI agents. Drawing on the theoretical notions of customer experience (CX) management, personalization, and AI in service, we develop a framework to design conversational AI agents. We propose a six-stage iterative design for conversational AI agents that begins with sensing customer intent, adapting to journey context, assigning tone to the conversation, delegating to humans, orchestrating processes to service requests and training of AI agents to adaptively improve and loopback into prior design stages. Additionally, we recognize that firms need to holistically qualify and allocate service requests to such conversational AI agents based on the firm’s purpose and CX strategy.
Sustainable Information Security Sensitization in SMEs: Designing Measures with Long-Term Effect

This paper outlines an overall scenario for ongoing personnel development measures designed to increase information security awareness in small and medium-sized enterprises (SMEs) in Germany and to help small businesses improve their security levels and defenses. The three-year project combines different actors and a multitude of methods, with a focus on conducting interviews and online surveys with companies, developing customized game-based awareness trainings, tests, and on-site attacks, and creating measurements and evaluations as well as maturity statements, guidelines, and low-threshold security concepts. A mix of analog/digital serious games and operational trainings with reviews are of key importance here. Compared with the findings from the applied scientific literature on behavioral research and design, the ultimate goal at project’s end is to extrapolate statements on the success and efficacy of the measures and their long-term effect.
Reaching an Underserved Population in Communities: Project Xander – Cybersecurity for NonProfits

Cyber criminals continue to look for new targets which they can exploit. A recent addition to their list of targets are nonprofit and faith-based organizations within communities. These organization generally do not have the budget to hire a cybersecurity professional or pay for cybersecurity services. A program to reach this sector to assist them with their cybersecurity posture was launched called Project Xander. This program is designed to utilize students from area colleges and universities to provide assessment and consulting services for organizations within the sector. The pilot program was run in the 2021-2022 academic year and was successful and is now being expanded to include other communities and academic institutions. It addresses a need in communities in a sector that has been underserved and that has historically not had the resources to implement robust cybersecurity programs.
I 🌹🍀🇫🇮 You! - Emojis as Emotional-Political Signifiers in Finnish Election Campaign Discussion Online

Social media platforms and the forms of vernacular expression they support invite citizens to emotionally react to political content. This study focuses on the affective networks of emoji use in the Facebook comment threads on 18 politicians’ public pages before the Finnish 2019 parliamentary elections. We aim to generate a holistic understanding of the use of emojis in campaign communications while providing a more profound understanding of the affective practices emerging in specific contexts, such as in relation to particular political actors. We analyze the data using computational methods and network analysis and complement this with qualitative analysis. The results indicate that emojis are used to connect with ideological coalitions across the parties and to reinforce connections with political actors. Particular emojis become distinctive emotional-political signifiers whose connotations have departed from their original denotations and are formed and shared in specific communities in relation to specific actors.
Unified Explanations in Machine Learning Models: A Perturbation Approach

A high-velocity paradigm shift towards Explainable Artificial Intelligence (XAI) has emerged in recent years. Highly complex Machine Learning (ML) models have flourished in many tasks of intelligence, and the questions have started to shift away from traditional metrics of validity towards something deeper: What is this model telling me about my data, and how is it arriving at these conclusions? Previous work has uncovered predictive models generating explanations contrasting domain experts, or excessively exploiting bias in data that renders a model useless in highly-regulated settings. These inconsistencies between XAI and modeling techniques can have the undesirable effect of casting doubt upon the efficacy of these explainability approaches. To address these problems, we propose a systematic, perturbation-based analysis against a popular, model-agnostic method in XAI, SHapley Additive exPlanations (Shap). We devise algorithms to generate relative feature importance in settings of dynamic inference amongst a suite of popular machine learning and deep learning methods, and metrics that allow us to quantify how well explanations generated under the static case hold. We propose a taxonomy for feature importance methodology, measure alignment, and observe quantifiable similarity amongst explanation models across several datasets.
Understanding the Role of Leadership Competencies in Cyber Crisis Management: A Case Study

The amount and severity of cyber-attacks has been constantly increasing in recent years, and the number of cyber-related organizational crises grew accordingly. Despite the relevance of the topic, the literature on the subject is still limited, especially from a non-technical point of view. In the context of leadership, traditional crisis management literature identified specific competencies that organizations can leverage to mitigate the effects of a crisis, but there is a research gap as to whether or not these capabilities make sense in a cyber crisis context. This study aims to bridge this gap by analyzing the case of Norsk Hydro – a Norwegian company that in 2019 fell victim of a disruptive ransomware attack – through the lenses of a traditional crisis leadership model.
Connecting the Dots: An Assessment of Cyber-risks in Networked Building and Municipal Infrastructure Systems

The buildings and city streets we walk down are changing. Driven by various data-driven use cases, there is increased interest in networking and integrating lighting and other building systems (e.g., heating, ventilation, and air conditioning (HVAC), security, scheduling) that were previously not internet-facing, and equipping them with sensors that collect information about their environment and the people that inhabit it. These data-enabled systems can potentially deliver improved occupant and resident experiences and help meet the U.S. Department of Energy (DOE) national energy and carbon reduction goals. Deploying connected devices new to being networked, however, is not without its challenges. This paper explores tools available to system designers and integrators that facilitate a cybersecurity landscape assessment – or more specifically the identification of threats, vulnerabilities, and adversarial behaviors that could be used against these networked systems. These assessments can help stakeholders shift security prioritization proactively toward the beginning of the development process.
Conversation Analytics: Can Machines Read between the Lines in Real-Time Strategic Conversations?

Strategic conversations involve one party with an informational advantage and the other with an interest in the information. This paper proposes machine-learning based measures to quantify the degrees of evasiveness and incoherence of the informed party during real-time strategic conversations. The specific empirical context is the questions and answers (Q&A) part of earnings conference calls during which managers endure high pressure as they face analysts’ scrutinizing questions. Being reluctant to disclose adverse information, managers may resort to evasive answers and sometimes respond less coherently due to increased cognitive load. Using data from the earnings calls of the S&P 500 companies from 2006 to 2018, we show that the proposed measures predict worse next-quarter earnings. Moreover, the stock market perceives incoherence as a negative signal. This paper contributes methodologically by developing two novel machine-powered measures to automatically evaluate behavioral cues during real-time strategic conversations. The proposed analytical tools are particularly beneficial to resource-constrained and informationally disadvantaged parties such as retail investors who may not be able to effectively trade on signals buried deep in unstructured conversational data.
Cybersecurity Governance – An Adapted Practical Framework for Small Enterprises

Digitalization is advancing and the associated risks are a strategic task for enterprises of all sizes. One risk area to which small businesses often do not pay enough attention are cyber risks. Often, the governance of cyber risks is not embedded at the owner or management level. However, it is important to evaluate, direct and monitor cyber risk mitigation activities by a company's leaders or its owner. A ´cybersecurity governance framework´ for small enterprises was developed and validated by applying Design Science Research. The framework focuses on criteria that are essential for small businesses, such as simplicity of understanding and ease of use (both for non-experts). Six principles identified relevant build the common thread of the framework, which guides the main activities to be implemented: 'responsibility', 'strategy', 'cybersecurity threats and risks', 'development and change', 'conformance' and 'people, skills and competencies'.
Explaining Explainable Artificial Intelligence: An integrative model of objective and subjective influences on XAI

Explainable artificial intelligence (XAI) is a new field within artificial intelligence (AI) and machine learning (ML). XAI offers a transparency of AI and ML that can bridge the gap in information that has been absent from “black-box” ML models. Given its nascency, there are several taxonomies of XAI in the literature. The current paper incorporates the taxonomies in the literature into one unifying framework, which defines the types of explanations, types of transparency, and model methods that together inform the user’s processes towards developing trust in AI and ML systems.
Digital Security Governance: What Can We Learn from High Reliability Organizations (HROs)?

With the growing digitalization of businesses, digital security governance (DSG) is becoming central to organizational survival strategies. However, many organizations fail to establish successful DSG practices and, consequently, fail to understand how DSG can lower the severity of cybersecurity failures. This paper aims to contribute to filling this gap. By putting the five principles of the High Reliability Organization (HRO) central to the design of our qualitative investigation, we engage in interviewing forty-two chief information security officers (CISOs) and chief information officers (CIOs) of large organizations in the Netherlands about their views on why organizations fail to successfully achieve DSG. Our data show that HRO principles are partly relevant but lacking in DSG approaches, which potentially increases security failure. We conclude this paper by discussing these findings in light of future research and practice.
Learnings from Implementing a Pilot Hybrid Question Answering System for a Government Agency in Singapore

The Singapore Government first released its digital government blueprint in 2018 with the key message for all agencies to be "digital to the core and served with heart". With this push, agencies are moving towards human-centric digital services to handle the surge in inquiries from individual citizens. One solution that is widely adopted is the use of chatbot technology that directly interfaces with the customer. However, several organizations have faced backlash from the citizens when such chatbots are unable to answer or give inappropriate answers to the questions. Hence this research explores a different approach to address this challenge using a question answering (QA) system that supports the CSOs to help answer the citizen inquiries more efficiently. This paper shares our preliminary learnings from the implementation of the pilot QA system, the Citizen Question Answering System (CQAS), and proposes areas for future work to enhance QA systems. This research work contributes to the body of artificial intelligence (AI) applied research in a government setting.
Capturing the Dynamic Nature of Cyber Risk: Evidence from an Explorative Case Study

In this research, we developed a novel approach to enable a dynamic cyber risk management strategy as the dynamic nature of cyber risk is rarely considered in current decision support tools. Our explorative case study shows that many management challenges such as investment decisions, priority setting, and “shelf time” analyses can be continuously analyzed. Our research using system thinking and modelling provides valuable insights about these challenges to support current strategic decision-making practices and improve managerial learning. These insights enable management to identify and analyze the effectiveness of future cyber risk management strategies before implementing them.
Fame! I wanna stream forever! Analysis and Critique of Successful Streamers' Advice to the Next Generation

This paper examines the practice of large-scale and successful streamers in creating ‘how-to’ videos with advice for smaller streamers and content creators. The goal of this study was to investigate how successful streamers encourage newcomers and/or smaller streamers to “grow” on Twitch, examining such factors as the advice they offer, the evidence that such advice works, and their reasons for sharing such information. More critically, how do their materials invoke rhetorics of meritocracy, and how does the creation of such videos further entrench those creators as successful, able to deploy “streaming capital” to buttress their claims? While the initial focus was on Twitch, this research led to a wider ranging exploration as the advice of successful streamers was to employ other platforms in combination, as the field of live streaming has extended far beyond Twitch itself.
Conflict Resolution in an ISO/IEC 27001 Standard Implementation: A Contradiction Management Perspective

The ISO/IEC 27001 standard provides organizations with guidelines to help them evaluate, document, and improve their information security processes. In practice, however, the generality of the standard can create a conflict between its requirements and the adopters’ expectations. To better understand how an organization manages such conflicts, we conduct a case study in a Finnish corporation during the standard’s implementation in one of its units. Two critical conflicts emerged: Conflict I reflects a tension between the standard requirement for disciplinary measures vis-à-vis the organization’s punishment-averse culture. Conflict II reflects a tension between the organization’s aspiration for concrete code reviewing instructions vis-à-vis the lack thereof in the standard. Our findings reveal that whereas the conflict resolution process was similar in managing both conflicts, their content was radically different. Specifically, whereas conflict I’s resolution was paradoxical, conflict II’s resolution was dialectical. We discuss the theoretical and practical implications of our findings.
Your Sentiment Matters: A Machine Learning Approach for Predicting Regime Changes in the Cryptocurrency Market

Research suggests that a significant number of those investing in cryptocurrencies do not follow what we might call rational, profit-maximizing behavior. We also know that with the progressive lowering of entry barriers to online trading platforms, an increasing number of inexperienced investors are investing in cryptocurrencies. Increasingly, the behavior of investors contradicts the predictions made by traditional financial models and challenges the assumptions on which such models have previously relied when anticipating returns on cryptocurrency investments. To overcome this issue we develop a random forest model which we train with features stemming from a sentiment analysis performed on data generated by cryptocurrency enthusiasts using Twitter, Google Trends, and Reddit. Our findings show that such features have an important role to play in capturing the behavior of cryptocurrency investors and increase our model’s ability to anticipate regime changes in the cryptocurrency market. Our model outperforms the predictive ability of the Log-Periodic Power Law model—currently, the model most widely-used to predict regime changes in financial markets. These results imply that scholars and practitioners aiming to understand and predict the development of cryptocurrency markets stand to benefit from analyzing social media data generated by cryptocurrency enthusiasts.
Examining the Use of Blockchain Technology in Virtual Worlds: A Socio-Technical Systems Perspective

Recent developments, including the rebranding of Facebook to Meta, have led to large-scale media attention on the phenomenon of the Metaverse. Although not being a new phenomenon in Information Systems (IS) research, many intricacies in virtual worlds remain unexplored. In particular , prior research has directed attention to users‘ lack of ownership rights, creating tension between the creator and user. To solve this tension, we argue that blockchain technology can potentially help to structure ownership rights. Therefore, our research explores a blockchain-based Metaverse through the socio-technical system lens. Our study highlights that, in the underlying case, the use of blockchain technology goes beyond the application of Non- Fungible Tokens (NFTs) and can also be found in the general organizational structure, blurring the boundaries of existing stakeholders. Our work contributes to research by providing a gaze into blockchain-based Metaverses and highlighting the potential application and benefit of the technology in virtual worlds.
A Hybrid Job Scheduling Approach on Cloud Computing Environments on the Usage of Heuristics and Metaheuristics Methods

The Information Technology Industry has been revolutionized through Cloud Computing by offering dynamic computing services to users through its on-demand provisioning of scalable and virtualized resources over the internet on a pay-per-use measured basis. Performance improvements in task scheduling can have a great impact on the efficiency of cloud computing. This paper proposes a hybrid task scheduling approach which employs the metaheuristic optimization technique, genetic algorithm to produce a certain combination of scheduling heuristics for processing cloud workloads. This approach is developed to optimize the performance metrics namely makespan, average flow time, throughput, and average waiting time. The developed approach is evaluated on the CloudSimPlus simulation framework using large-scale benchmarks against other heuristics in terms of the stated performance metrics. The results indicate that the proposed hybrid approach consistently outperforms the baseline individual heuristics in terms of the stated metrics irrespective of the scale of the workload. It is also observed that the optimization potential tends to increase as the workload scale becomes heavier and optimizing flow time produces complementary effects on the other metrics.
Data Sovereign Humans and the Information Economy: Towards Design Principles for Human Centric B2C Data Ecosystems

The ever-growing amounts of data offer companies many opportunities for data-driven-value generation which, in turn, can be multiplied by leveraging data across company boundaries in evolving data ecosystems. However, while such systems increasingly emerge in B2B environments enabling systematic sharing and utilization of “industrial data”, comparable concepts in B2C ambits have not yet prevailed. Despite the rising importance of personal data in the information economy, B2C data ecosystems represent a widely unexplored research area. To remedy this gap, the study generates design principles for human centric B2C data ecosystems to aid in their development. For this purpose, a qualitative interview study with experts of interdisciplinary domains and a structured literature review are conducted both embedded into a methodology for generating design principles. On this basis, derived design principles help to understand peculiarities of data ecosystems in B2C ambits and provide solutions to overcome their obstacles identified in the empirical investigation.