A user's ownership perception of virtual objects, such as cloud files, is generally uncertain. Is this valid for streaming platforms featuring accounts designed for sharing (DS)? We observe sharing practices within DS accounts of streaming platforms and identify their ownership characteristics and unexpected complications through two mixed-method studies. Casual and Cost-splitting are the two sharing practices identified. The owner is the sole payer for the account in the former, whereas profile holders split the cost in the latter. We distinguish two types of ownership in each practice -- Primary and Dual. In Primary ownership, the account owner has the power to allow others to use the account; in Dual ownership, Primary ownership appears in conjunction with joint ownership, notably displaying asymmetric ownership perceptions among users. Conflicts arise when the sharing agreements collapse. Therefore, we propose design recommendations that bridge ownership differences based on sharing practices of DS accounts.
The Industrial Internet of Things (IIoT) requires networks that deliver ultra-low latency, high reliability, and cost efficiency, which traditional optimization methods and deep reinforcement learning (DRL)-based approaches struggle to provide under dynamic and heterogeneous workloads. To address this gap, large language model (LLM)-empowered agentic AI has emerged as a promising paradigm, integrating reasoning, planning, and adaptation to enable QoE-aware network management. In this paper, we explore the integration of agentic AI into QoE-aware network slicing for IIoT. We first review the network slicing management architecture, QoE metrics for IIoT applications, and the challenges of dynamically managing heterogeneous network slices, while highlighting the motivations and advantages of adopting agentic AI. We then present the workflow of agentic AI-based slicing management, illustrating the full lifecycle of AI agents from processing slice requests to constructing slice instances and performing dynamic adjustments. Furthermore, we propose an LLM-empowered agentic AI approach for slicing management, which integrates a retrieval-augmented generation (RAG) module for semantic intent inference, a DRL-based orchestrator for slicing configuration, and an incremental memory mechanism for continual learning and adaptation. Through a case study on heterogeneous slice management, we demonstrate that the proposed approach significantly outperforms other baselines in balancing latency, reliability, and cost, and achieves up to a 19% improvement in slice availability ratio.
In Industry 4.0 applications, dynamic environmental interference induces highly nonlinear and strongly coupled interactions between the environmental state and robotic behavior. Effectively representing dynamic environmental states through multimodal sensor data fusion remains a critical challenge in current robotic datasets. To address this, an industrial-grade multimodal interference dataset is presented, designed for robotic perception and control under complex conditions. The dataset integrates multi-dimensional interference features including size, color, and lighting variations, and employs high-precision sensors to synchronously collect visual, torque, and joint-state measurements. Scenarios with geometric similarity exceeding 85\% and standardized lighting gradients are included to ensure real-world representativeness. Microsecond-level time-synchronization and vibration-resistant data acquisition protocols, implemented via the Robot Operating System (ROS), guarantee temporal and operational fidelity. Experimental results demonstrate that the dataset enhances model validation robustness and improves robotic operational stability in dynamic, interference-rich environments. The dataset is publicly available at:https://modelscope.cn/datasets/Liaoh_LAB/Liaohe-CobotMagic-PnP.
The global shift toward electric vehicles (EVs) for climate sustainability lacks comprehensive insights into the impact of the built environment on EV ownership, especially in varying spatial contexts. This study, focusing on New York State, integrates data fusion techniques across diverse datasets to examine the influence of socioeconomic and built environmental factors on EV ownership. The utilization of spatial regression models reveals consistent coefficient values, highlighting the robustness of the results, with the Spatial Lag model better at capturing spatial autocorrelation. Results underscore the significance of charging stations within a 10-mile radius, indicative of a preference for convenient charging options influencing EV ownership decisions. Factors like higher education levels, lower rental populations, and concentrations of older population align with increased EV ownership. Utilizing publicly available data offers a more accessible avenue for understanding EV ownership across regions, complementing traditional survey approaches.
Human-computer interaction scholars are increasingly touching on topics related to politics or democracy. As these concepts are ambiguous, an examination of concepts' invoked meanings aids in the self-reflection of our research efforts. We conduct a thematic analysis of all papers with the word `politics' in abstract, title or keywords ($n$=378) and likewise 152 papers with the word `democracy.' We observe that these words are increasingly being used in human-computer interaction, both in absolute and relative terms. At the same time, we show that researchers invoke these words with diverse levels of analysis in mind: the early research focused on mezzo-level (i.e., small groups), but more recently the work has begun to include macro-level analysis (i.e., society and politics as played in the public sphere). After the increasing focus on the macro-level, we see a transition towards more normative and activist research, in some areas it replaces observational and empirical research. These differences indicate semantic differences, which -- in the worst case -- may limit scientific progress. We bring these differences visible to help in further exchanges of ideas and human-computer interaction community to explore how it orients itself to politics and democracy.
Yasaman Modaresi, Mir Ali Seyed Naghavi, Habib Roodsaz
et al.
Background & Purpose: Today, the digital revolution has fundamentally changed the nature of work, organizational boundaries, and the responsibilities of employees of organizations. Digital transformation is the right answer to a world full of change, ambiguity, and uncertainty; Accordingly, the organizations are required to be equipped with digital transformation to keep staying in the field of competition. Since digital transformation is not only related to new digital technologies and is more dependent on human beings, which is called the soft part of digital transformation, managers should be familiar with the soft components of digital transformation, which are necessary for the implementation of a great digital transformation and apply them in their organizations. Thus, the aim of the current research is to identify the soft components of digital transformation, and then design a conceptual framework to explain them.Methodology: This research was qualitative conducted using thematic analysis. The research source of information was made up of databases and qualified international journals in this field, and 43 participants related to the subject under study were selected using the purposeful sampling method.Findings: During the process of analyzing, interpreting, and synthesizing the findings, the conceptual framework resulting from the soft components of digital transformation includes the comprehensive theme of culture with 12 organizing themes and 50 basic themes; The comprehensive theme of skills has 4 organizing themes and 31 basic themes; Also, the overarching theme of the organization manager has 3 organizing themes and 24 basic themes, and finally the inclusive theme of digital leadership has 2 organizing themes and 12 basic themes.Conclusion: The identified soft components of digital transformation indicate that for success in digital transformation, organizations rely on the management of the organization as the one who steers the ship of the organization, and it is also the digital leader who inspires the passion for digital transformation. It creates in people and becomes the pioneer and driver of this transformation in the organization, and by creating a suitable culture, it paves the way for digital transformation. All of these factors are fruitful along with people and their skills; Therefore, an organization that is equipped with all these soft components can claim success in the field of digital transformation.
Employee participation in management. Employee ownership. Industrial democracy. Works councils
Masood Sepahvand, Mohamad Hakkak, Reza Sepahvand
et al.
Purpose: This research was conducted with the goal of identifying the causes and roots of Cronyism in governmental organizations of Lorestan Province.
Methodology: The present research was a survey-type regarding purpose, it was descriptive from the viewpoint of data collection method, and with regard to the nature of data, it was a qualitative research based on the grounded theory. The statistical population was comprised of governmental organizations’ senior managers with at least 4 years of managerial experience and with academic education at doctoral level in the fields of management, economics, and law. Applying theoretical sampling method and saturation rule, we selected fifteen managers. The required data were collected using semi-structured interviews.
Findings: Results show that 183 identified open codes were combined in the framework of 93 subcategories and 25 main categories, which were then shaped into 6 main layers including causal conditions, central category, contextual conditions, intervening conditions, strategies and consequences.
Originality: Presenting a model for organizational cronyism, which includes the most important and underlying factors in the fields of nepotism and gangsterism, the present research can help policymakers and researchers find out the roots of these factors and subsequently, implement strategies and mechanisms to overcome the negative consequences.
Implications: Paying attention to policy issues, transparency, accountability, reconsidering performance evaluation programs, and finally, enhancing meritocracy based on objective criteria can largely prevent the occurrence of cronyism in the organization.
Economic growth, development, planning, Employee participation in management. Employee ownership. Industrial democracy. Works councils
Ali Akbar Zamandi, Mohammad hassani, Hasan Galavandi
Purpose: The aim of the present study was to identify and prioritize the components of university social responsibilities.
Methodology: This research applied the mixed qualitative and quantitative approach. The statistical population consisted of the faculty members of Shahid Beheshti University. Using the purposive sampling method based on the principle of theoretical adequacy, 20 of them were selected as the sample. In the qualitative section, university social responsibility components were obtained from exploratory interviews and the data were analysed by Maxqda software as well as the coding method, Then, in the quantitative section, questionnaires were administered and were validated through relative content validity and re-test reliability. Here, the data were analysed and ranked by the fuzzy Delphi method.
Findings: The results fall into two parts. In the first part, a set of components of social responsibility was identified, and in the second part, the importance and priority of these factors are introduced. Findings indicate that the issue of social responsibility, understandability of social responsibility, transparency in decision making, accountability, academic culture building, and scientific correction are the most important components of social responsibility.
Originality: The use of mixed research method in the present study led to the identification and prioritization of new components of social responsibility in the university setting as an organization and social institution which is a driving force of development.
Economic growth, development, planning, Employee participation in management. Employee ownership. Industrial democracy. Works councils
Fiona Draxler, Anna Werner, Florian Lehmann
et al.
Human-AI interaction in text production increases complexity in authorship. In two empirical studies (n1 = 30 & n2 = 96), we investigate authorship and ownership in human-AI collaboration for personalized language generation. We show an AI Ghostwriter Effect: Users do not consider themselves the owners and authors of AI-generated text but refrain from publicly declaring AI authorship. Personalization of AI-generated texts did not impact the AI Ghostwriter Effect, and higher levels of participants' influence on texts increased their sense of ownership. Participants were more likely to attribute ownership to supposedly human ghostwriters than AI ghostwriters, resulting in a higher ownership-authorship discrepancy for human ghostwriters. Rationalizations for authorship in AI ghostwriters and human ghostwriters were similar. We discuss how our findings relate to psychological ownership and human-AI interaction to lay the foundations for adapting authorship frameworks and user interfaces in AI in text-generation tasks.
Johannes G. Hoffer, Sascha Ranftl, Bernhard C. Geiger
We consider the problem of finding an input to a stochastic black box function such that the scalar output of the black box function is as close as possible to a target value in the sense of the expected squared error. While the optimization of stochastic black boxes is classic in (robust) Bayesian optimization, the current approaches based on Gaussian processes predominantly focus either on i) maximization/minimization rather than target value optimization or ii) on the expectation, but not the variance of the output, ignoring output variations due to stochasticity in uncontrollable environmental variables. In this work, we fill this gap and derive acquisition functions for common criteria such as the expected improvement, the probability of improvement, and the lower confidence bound, assuming that aleatoric effects are Gaussian with known variance. Our experiments illustrate that this setting is compatible with certain extensions of Gaussian processes, and show that the thus derived acquisition functions can outperform classical Bayesian optimization even if the latter assumptions are violated. An industrial use case in billet forging is presented.
As open-source AI software projects become an integral component in the AI software development, it is critical to develop a novel methods to ensure and measure the security of the open-source projects for developers. Code ownership, pivotal in the evolution of such projects, offers insights into developer engagement and potential vulnerabilities. In this paper, we leverage the code ownership metrics to empirically investigate the correlation with the latent vulnerabilities across five prominent open-source AI software projects. The findings from the large-scale empirical study suggest a positive relationship between high-level ownership (characterised by a limited number of minor contributors) and a decrease in vulnerabilities. Furthermore, we innovatively introduce the time metrics, anchored on the project's duration, individual source code file timelines, and the count of impacted releases. These metrics adeptly categorise distinct phases of open-source AI software projects and their respective vulnerability intensities. With these novel code ownership metrics, we have implemented a Python-based command-line application to aid project curators and quality assurance professionals in evaluating and benchmarking their on-site projects. We anticipate this work will embark a continuous research development for securing and measuring open-source AI project security.
Background & Purpose: With an increasing focus on identifying and assessing risks in organizations, risk management has become a key strategic priority. The purpose of this study is to investigate a neglected field, namely HRM. Thus, this paper has studied the HR risk management in a public organization.
Methodology: Since the purpose of this study is to disseminate existing knowledge in the field of human resource risk, this study is developmental in terms of purpose; and due to the nature of the data, it is a mixed study. The statistical community in the qualitative section includes scientific texts, and academic and organizational experts. The statistical population in the quantitative part also included all employees of the Tax Affairs Department of West Azerbaijan Province, numbering 812 people. The measurement tool was a semi-structured interview and a questionnaire.
Findings: In the qualitative part, using the theme analysis method, dimensions, components and HR risk indicators were identified. In the quantitative part, using confirmatory factor analysis, while examining and confirming the indicators and dimensions of HR risk model, their priority in the West Azerbaijan Tax Affairs Organization was examined, so that the managers of the organization Plan to control the most important HR risks. The proposed model has 4 dimensions, 14 components and 40 indicators.
Conclusion: In this study, while identifying the dimensions, components and indicators of HR risk, the presented model was evaluated in a public organization. This evaluation enables the managers to identify and rank the status of the mentioned risks in the organization, to adjust the model indicators in a way that leads to the control of the risks in the organization.
Employee participation in management. Employee ownership. Industrial democracy. Works councils
Monire Alipour Madarsara, Gholamreza Memarzadeh Tehran, Mehdi Alvani
et al.
Background & Purpose: Focusing on the diversity of human capital in organizations and its possible role in the effectiveness of the compensation system, the purpose of this study was to predict the effectiveness of the compensation system for various types of human capital in Police Force of Qazvin Province.Methodology: The present research is descriptive, and it is applied in terms of purpose. The statistical population of the study includes 53 senior, middle, and operational managers of Qazvin Police Force, of which it was possible to access the opinions of 46 of them. The assessment tool was a questionnaire. In order to analyze the data, network analysis methods and fuzzy inference system were used.Findings: According to the results, the effectiveness of the service compensation system varies according to its types of human capital. Priority of the elements of compensation for core human capital included perquisites, rewards, salaries, benefits, welfare services, awards; for idiosyncratic human capital: salaries, benefits, perquisites, rewards, welfare services, awards; for compulsory human capital: salaries, benefits, welfare services, rewards, awards, perquisites; for ancillary human capital: benefits, rewards, salaries, welfare services, awards, perquisites respectively. Also, the evaluation of the model in the police force suggested that the most observed effectiveness belongs to compulsory human capital.Conclusion: The most important achievement of this article is designing an effective compensation system in the Police Force, which will dynamically determine the amount of output based to the amount of inputs. This model can be used to predict the effectiveness of the service compensation system.
Employee participation in management. Employee ownership. Industrial democracy. Works councils
Liquid democracy is a form of transitive delegative democracy that has received a flurry of scholarly attention from the computer science community in recent years. In its simplest form, every agent starts with one vote and may have other votes assigned to them via delegation from other agents. They can choose to delegate all votes assigned to them to another agent or vote directly with all votes assigned to them. However, many proposed realizations of liquid democracy allow for agents to express their delegation/voting preferences in more complex ways (e.g., a ranked list of potential delegates) and employ a centralized delegation mechanism to compute the final vote tally. In doing so, centralized delegation mechanisms can make decisions that affect the outcome of a vote and where/whether agents are able to delegate their votes. Much of the analysis thus far has focused on the ability of these mechanisms to make a correct choice. We extend this analysis by introducing and formalizing other important properties of a centralized delegation mechanism in liquid democracy with respect to crucial features such as accountability, transparency, explainability, fairness, and user agency. In addition, we evaluate existing methods in terms of these properties, show how some prior work can be augmented to achieve desirable properties, prove impossibility results for achieving certain sets of properties simultaneously, and highlight directions for future work.
Deepika Saxena, Ishu Gupta, Ashutosh Kumar Singh
et al.
Cloud computing has become inevitable for every digital service which has exponentially increased its usage. However, a tremendous surge in cloud resource demand stave off service availability resulting into outages, performance degradation, load imbalance, and excessive power-consumption. The existing approaches mainly attempt to address the problem by using multi-cloud and running multiple replicas of a virtual machine (VM) which accounts for high operational-cost. This paper proposes a Fault Tolerant Elastic Resource Management (FT-ERM) framework that addresses aforementioned problem from a different perspective by inducing high-availability in servers and VMs. Specifically, (1) an online failure predictor is developed to anticipate failure-prone VMs based on predicted resource contention; (2) the operational status of server is monitored with the help of power analyser, resource estimator and thermal analyser to identify any failure due to overloading and overheating of servers proactively; and (3) failure-prone VMs are assigned to proposed fault-tolerance unit composed of decision matrix and safe box to trigger VM migration and handle any outage beforehand while maintaining desired level of availability for cloud users. The proposed framework is evaluated and compared against state-of-the-arts by executing experiments using two real-world datasets. FT-ERM improved the availability of the services up to 34.47% and scales down VM-migration and power-consumption up to 88.6% and 62.4%, respectively over without FT-ERM approach.
Masoud Taherinia, Ali Shariat Najade, Sayedh Nasim Mousavi
et al.
Background & Purpouse: The success of knowledge-based organizations requires the promotion of creativity and innovation, and sometimes it requires the courage to act beyond the redundant and cumbersome bureaucratic rules. Thus, the present study has dealt with the role of authoritarian leadership in encouraging creative deviation among employees by encouraging them to dare. Methodology: This is an applied and descriptive survey research. The statistical population consisted of employees of knowledge-based organizations in Lorestan Province, Iran, of whom 309 employees were selected as a statistical sample. The data collection tool used in this study was a questionnaire. In fact, its validity was assessed by content validity method, and its reliability was evaluated using Cronbach's alpha method. The data was analyzed by structural equation modeling using Smart Software Pilates and SPSS. Findings: Authoritarian leaders make a positive and significant effect on moral deviation both directly and through employees' assertiveness. Conclusion: Managers of knowledge-based organizations can strengthen the capacity and daring of employees to think and act beyond the existing bureaucratic constraints, and thus this will lead to enhancing their creative and innovative performance.
Employee participation in management. Employee ownership. Industrial democracy. Works councils
Maintaining the stability of the modern power grid is becoming increasingly difficult due to fluctuating power consumption, unstable power supply coming from renewable energies, and unpredictable accidents such as man-made and natural disasters. As the operation on the power grid must consider its impact on future stability, reinforcement learning (RL) has been employed to provide sequential decision-making in power grid management. However, existing methods have not considered the environmental constraints. As a result, the learned policy has risk of selecting actions that violate the constraints in emergencies, which will escalate the issue of overloaded power lines and lead to large-scale blackouts. In this work, we propose a novel method for this problem, which builds on top of the search-based planning algorithm. At the planning stage, the search space is limited to the action set produced by the policy. The selected action strictly follows the constraints by testing its outcome with the simulation function provided by the system. At the learning stage, to address the problem that gradients cannot be propagated to the policy, we introduce Evolutionary Strategies (ES) with black-box policy optimization to improve the policy directly, maximizing the returns of the long run. In NeurIPS 2020 Learning to Run Power Network (L2RPN) competition, our solution safely managed the power grid and ranked first in both tracks.
As an industry of primarily small and mid-size businesses, it is becoming increasingly more difficult for British Columbia (BC)'s tree fruit growers to compete with large, often vertically integrated producers from other regions. New ways of managing information and resources collaboratively are needed to develop competitive strengths. This case study seeks to understand the information and knowledge management capabilities of the BC tree fruit cluster across the value chain for six different data domains. A qualitative methodology design of 21 in-depth interviews with cluster stakeholders provides insights into the data quality, completeness and integration points, and then applies CMMI level criteria to assess the information management capabilities of the industry. Significant data and process gaps are identified. This paper explores how the BC tree fruit industry can move forward from this position using technology solutions to support the development of information and knowledge, and collective decision-making.
Task-parallel programs often enjoy deadlock freedom under certain restrictions, such as the use of structured join operations, as in Cilk and X10, or the use of asynchronous task futures together with deadlock-avoiding policies such as Known Joins or Transitive Joins. However, the promise, a popular synchronization primitive for parallel tasks, does not enjoy deadlock-freedom guarantees. Promises can exhibit deadlock-like bugs; however, the concept of a deadlock is not currently well-defined for promises. To address these challenges, we propose an ownership semantics in which each promise is associated to the task which currently intends to fulfill it. Ownership immediately enables the identification of bugs in which a task fails to fulfill a promise for which it is responsible. Ownership further enables the discussion of deadlock cycles among tasks and promises and allows us to introduce a robust definition of deadlock-like bugs for promises. Cycle detection in this context is non-trivial because it is concurrent with changes in promise ownership. We provide a lock-free algorithm for precise runtime deadlock detection. We show how to obtain the memory consistency criteria required for the correctness of our algorithm under TSO and the Java and C++ memory models. An evaluation compares the execution time and memory usage overheads of our detection algorithm on benchmark programs relative to an unverified baseline. Our detector exhibits a 12% (1.12$\times$) geometric mean time overhead and a 6% (1.06$\times$) geometric mean memory overhead, which are smaller overheads than in past approaches to deadlock cycle detection.