Hasil untuk "Public relations. Industrial publicity"

Claire Liang, Franziska Babel, Hannah Pelikan et al.

Many of the challenges encountered in in-the-wild public deployments of robots remain undocumented despite sharing many common pitfalls. This creates a high barrier of entry and results in repetition of avoidable mistakes. To articulate the tacit knowledge in the HRI community, this paper presents a guideline in the form of a checklist to support researchers in preparing for robot deployments in public. Drawing on their own experience with public robot deployments, the research team collected essential topics to consider in public HRI research. These topics are represented as modular flip cards in a hierarchical table, structured into deployment phases and important domains. We interviewed six interdisciplinary researchers with expertise in public HRI and show how including community input refines the checklist. We further show the checklist in action in context of real public studies. Finally, we contribute the checklist as an open-source, customizable community resource that both collects joint expertise for continual evolution and is usable as a list, set of cards, and an interactive web tool.

Jing Chen, Wentao Zhou

With the rise of smart contracts, decentralized autonomous organizations (DAOs) have emerged in public good auctions, allowing "small" bidders to gather together and enlarge their influence in high-valued auctions. However, models and mechanisms in the existing research literature do not guarantee non-excludability, which is a main property of public goods. As such, some members of the winning DAO may be explicitly prevented from accessing the public good. This side effect leads to regrouping of small bidders within the DAO to have a larger say in the final outcome. In particular, we provide a polynomial-time algorithm to compute the best regrouping of bidders that maximizes the total bidding power of a DAO. We also prove that such a regrouping is less-excludable, better aligning the needs of the entire DAO and the nature of public goods. Next, notice that members of a DAO in public good auctions often have a positive externality among themselves. Thus we introduce a collective factor into the members' utility functions. We further extend the mechanism's allocation for each member to allow for partial access to the public good. Under the new model, we propose a mechanism that is incentive compatible in generic games and achieves higher social welfare as well as less-excludable allocations.

Devansh Saxena, Zoe Kahn, Erina Seh-Young Moon et al.

Local and federal agencies are rapidly adopting AI systems to augment or automate critical decisions, efficiently use resources, and improve public service delivery. AI systems are being used to support tasks associated with urban planning, security, surveillance, energy and critical infrastructure, and support decisions that directly affect citizens and their ability to access essential services. Local governments act as the governance tier closest to citizens and must play a critical role in upholding democratic values and building community trust especially as it relates to smart city initiatives that seek to transform public services through the adoption of AI. Community-centered and participatory approaches have been central for ensuring the appropriate adoption of technology; however, AI innovation introduces new challenges in this context because participatory AI design methods require more robust formulation and face higher standards for implementation in the public sector compared to the private sector. This requires us to reassess traditional methods used in this space as well as develop new resources and methods. This workshop will explore emerging practices in participatory algorithm design - or the use of public participation and community engagement - in the scoping, design, adoption, and implementation of public sector algorithms.

Zhanat Saduakassova, M. Kadyrova, Vainius Smalskys et al.

This work is aimed at forming a conceptual view of the introduction of artificial intelligence technologies into the sanitary and epidemiological monitoring system of Kazakhstan. This issue is particularly relevant in the context of global challenges manifested in rapid urbanization, industrial development, increasing international cooperation, the growth of trade relations, intensified migration flows, and the expansion of tourism. A major challenge for public health today is environmental change and the growth of anthropogenic factors that directly affect population health. The purpose of this research is to form a conceptual model and identify the main aspects of the implementing artificial intelligence in monitoring based on international experience. The materials and methods include the analysis of international publications, an expert survey among employees of sanitary and epidemiological service employees, and conceptual modeling of the integration of artificial intelligence into the sanitary and epidemiological monitoring system. Results and discussion: The analysis of the literature revealed numerous positive aspects of incorporating artificial intelligence into monitoring, while approaches to implementation are based on the needs of the field and managerial conditions. The results of expert opinions identified the main barriers within the sanitary and epidemiological service on the way to digitalization. The proposed conseptual model illustrates the potential of integrating artificial intelligence into the monitoring system in accordance with the principles of Data-Centric Governance. Keywords: artificial intelligence, digitalization, conceptual model, sanitary and epidemiological monitoring, public health, IT policy.

Massimiliano Andretta, Paola Imperatore

Using an industrial relations regime (IRR)‐centred framework, we analyse how employers, unions, public authorities and worker collectives frame ecological transition in automotive. Qualitative frame analysis (33 stakeholders and 22 worker interviews) in Italy, as an illustrative case, reveals models from market‐led green capitalism to just‐transition approaches, with workers favouring protection and democratic participation.

Darcy du Toit, Shane Godfrey

The article combines an empirical historical overview of sectoral bargaining with a legal analysis of the post-1994 legislative framework for sectoral bargaining. The empirical evidence provides strong indications that deindustrialisation and the burgeoning services sector are undermining industrial unionism and the system of sectoral bargaining, with the exception of centralised bargaining in the public service. The legal analysis builds on this assessment by highlighting the 1995 Labour Relations Act’s failure, although aiming to promote sectoral bargaining, to take account of the changes emerging in the labour market in its provisions for collective bargaining. While subsequent amendments have sought to catch up with labour market developments and shore up the bargaining council system in the private sector, they have fallen short. The root of the problem is the failure to provide a mechanism to include non-standard employees and other marginalised workers in the collective bargaining system, either via existing unions or by allowing scope for their organisations to participate in collective bargaining. The article ends by proposing some guidelines for a social dialogue process that would extend the reach of sectoral bargaining.

Yiwei Gao, Fang Zhou, Qing Gao et al.

The systems within the Industrial Internet of Things (IIoT) have complex structures and non-Euclidean data, which are challenging to manage. Due to the advantages of graph neural networks (GNNs) in processing non-Euclidean data and complex topologies, they are capable of handling problems in the context of the IIoT. In this work, the IIoT system is structured into multiple layers to facilitate the management of the system and the use of GNNs. GNNs are taken as node classifiers to analyze the state of each edge server in the IIoT system. However, in reality, adversarial attacks often arise in the IIoT, severely impacting system performance. Therefore, a black-box graph injection attack, Bayesian ridge regression injection attack (BRRIA), is proposed to study the impact of the internal relations on a system and to investigate the vulnerabilities of GNNs. Extensive experiments on two public datasets demonstrate the effectiveness of our attack method. In both experiments targeting specific victim nodes and those attacking a certain category of nodes by targeting critical nodes, BRRIA demonstrates a higher attack accuracy compared to an advanced method. Besides, a synthetic dataset designed to simulate industrial production processes was used to demonstrate the effectiveness of the BRRIA method.

Mirthe Dankloff, Vanja Skoric, Giovanni Sileno et al.

AI algorithms used in the public sector, e.g., for allocating social benefits or predicting fraud, often involve multiple public and private stakeholders at various phases of the algorithm's life-cycle. Communication issues between these diverse stakeholders can lead to misinterpretation and misuse of algorithms. We investigate the communication processes for AI fairness-related decisions by conducting interviews with practitioners working on algorithmic systems in the public sector. By applying qualitative coding analysis, we identify key elements of communication processes that underlie fairness-related human decisions. We analyze the division of roles, tasks, skills, and challenges perceived by stakeholders. We formalize the underlying communication issues within a conceptual framework that i. represents the communication patterns ii. outlines missing elements, such as actors who miss skills for their tasks. The framework is used for describing and analyzing key organizational issues for fairness-related decisions. Three general patterns emerge from the analysis: 1. Policy-makers, civil servants, and domain experts are less involved compared to developers throughout a system's life-cycle. This leads to developers taking on extra roles such as advisor, while they potentially miss the required skills and guidance from domain experts. 2. End-users and policy-makers often lack the technical skills to interpret a system's limitations, and rely on developer roles for making decisions concerning fairness issues. 3. Citizens are structurally absent throughout a system's life-cycle, which may lead to decisions that do not include relevant considerations from impacted stakeholders.

Molud Esmaili, Ken Christensen

Public permissionless blockchains facilitate peer-to-peer digital transactions, yet face performance challenges specifically minimizing transaction confirmation time to decrease energy and time consumption per transaction. Performance evaluation and prediction are crucial in achieving this objective, with performance modeling as a key solution despite the complexities involved in assessing these blockchains. This survey examines prior research concerning the performance modeling blockchain systems, specifically focusing on public permissionless blockchains. Initially, it provides foundational knowledge about these blockchains and the crucial performance parameters for their assessment. Additionally, the study delves into research on the performance modeling of public permissionless blockchains, predominantly considering these systems as bulk service queues. It also examines prior studies on workload and traffic modeling, characterization, and analysis within these blockchain networks. By analyzing existing research, our survey aims to provide insights and recommendations for researchers keen on enhancing the performance of public permissionless blockchains or devising novel mechanisms in this domain.

Alexander Windmann, Philipp Wittenberg, Marvin Schieseck et al.

In Industry 4.0, Cyber-Physical Systems (CPS) generate vast data sets that can be leveraged by Artificial Intelligence (AI) for applications including predictive maintenance and production planning. However, despite the demonstrated potential of AI, its widespread adoption in sectors like manufacturing remains limited. Our comprehensive review of recent literature, including standards and reports, pinpoints key challenges: system integration, data-related issues, managing workforce-related concerns and ensuring trustworthy AI. A quantitative analysis highlights particular challenges and topics that are important for practitioners but still need to be sufficiently investigated by academics. The paper briefly discusses existing solutions to these challenges and proposes avenues for future research. We hope that this survey serves as a resource for practitioners evaluating the cost-benefit implications of AI in CPS and for researchers aiming to address these urgent challenges.

Miguel Fuentes, Brett Mullins, Ryan McKenna et al.

Mechanisms for generating differentially private synthetic data based on marginals and graphical models have been successful in a wide range of settings. However, one limitation of these methods is their inability to incorporate public data. Initializing a data generating model by pre-training on public data has shown to improve the quality of synthetic data, but this technique is not applicable when model structure is not determined a priori. We develop the mechanism jam-pgm, which expands the adaptive measurements framework to jointly select between measuring public data and private data. This technique allows for public data to be included in a graphical-model-based mechanism. We show that jam-pgm is able to outperform both publicly assisted and non publicly assisted synthetic data generation mechanisms even when the public data distribution is biased.

Jasmina Karabegovic

This paper introduces an explicit algorithm for computing perfect public equilibrium (PPE) payoffs in repeated games with imperfect public monitoring, public randomization, and discounting. The method adapts the established framework by Abreu, Pearce, and Stacchetti (1990) into a practical tool that balances theoretical accuracy with computational efficiency. The algorithm simplifies the complex task of identifying PPE payoff sets for any given discount factor δ. A stand-alone implementation of the algorithm can be accessed at: https://github.com/jasmina-karabegovic/IRGames.git.

Doratossadat Dastgheib, Hadi Farahani

In this paper, we propose a doxastic extension $BL^+$ of Lukasiewicz logic which is sound and complete relative to the introduced corresponding semantics. Also, we equip our doxastic Lukasiewicz logic $BL^+$ with public announcement and propose the logic $DL$. As an application, we model a fuzzy version of muddy children puzzle with public announcement using $DL$. Finally, we define a translation between $DL$ and $BL^+$, and prove the soundness and completeness theorems for D L

Ananya Joshi, Kathryn Mazaitis, Roni Rosenfeld et al.

Irregularities in public health data streams (like COVID-19 Cases) hamper data-driven decision-making for public health stakeholders. A real-time, computer-generated list of the most important, outlying data points from thousands of daily-updated public health data streams could assist an expert reviewer in identifying these irregularities. However, existing outlier detection frameworks perform poorly on this task because they do not account for the data volume or for the statistical properties of public health streams. Accordingly, we developed FlaSH (Flagging Streams in public Health), a practical outlier detection framework for public health data users that uses simple, scalable models to capture these statistical properties explicitly. In an experiment where human experts evaluate FlaSH and existing methods (including deep learning approaches), FlaSH scales to the data volume of this task, matches or exceeds these other methods in mean accuracy, and identifies the outlier points that users empirically rate as more helpful. Based on these results, FlaSH has been deployed on data streams used by public health stakeholders.

Colin Megill, Elizabeth Barry, Christopher Small

Systems for large scale deliberation have resolved polarized issues and shifted agenda setting into the public's hands. These systems integrate bridging-based ranking algorithms - including group informed consensus implemented in Polis and the continuous matrix factorization approach implemented by Twitter Birdwatch - making it possible to highlight statements which enjoy broad support from a diversity of opinion groups. Polis has been productively employed to foster more constructive political deliberation at nation scale in law making exercises. Twitter Birdwatch is implemented with the intention of addressing misinformation in the global public square. From one perspective, Twitter Birdwatch can be viewed as an anti-misinformation system which has deliberative aspects. But it can also be viewed as a first step towards a generalized deliberative system, using Twitter's misinformation problem as a proving ground. In this paper, we propose that Twitter could adapt Birdwatch to produce maps of public opinion. We describe a system in five parts for generalizing Birdwatch: activation of a deliberative system and topic selection, population sampling and the role of expert networks, deliberation, reporting interpretable results and finally distribution of the results to the public and those in power.

Arumoy Shome, Luis Cruz, Arie van Deursen

The adoption of Artificial Intelligence (AI) in high-stakes domains such as healthcare, wildlife preservation, autonomous driving and criminal justice system calls for a data-centric approach to AI. Data scientists spend the majority of their time studying and wrangling the data, yet tools to aid them with data analysis are lacking. This study identifies the recurrent data quality issues in public datasets. Analogous to code smells, we introduce a novel catalogue of data smells that can be used to indicate early signs of problems or technical debt in machine learning systems. To understand the prevalence of data quality issues in datasets, we analyse 25 public datasets and identify 14 data smells.

Andrés Aldana, Andrea Falcón-Cortés, Hernán Larralde

The costs and impacts of government corruption range from impairing a country's economic growth to affecting its citizens' well-being and safety. Public contracting between government dependencies and private sector instances, referred to as public procurement, is a fertile land of opportunity for corrupt practices, generating substantial monetary losses worldwide. Thus, identifying and deterring corrupt activities between the government and the private sector is paramount. However, due to several factors, corruption in public procurement is challenging to identify and track, leading to corrupt practices going unnoticed. This paper proposes a machine learning model based on an ensemble of random forest classifiers, which we call hyper-forest, to identify and predict corrupt contracts in México's public procurement data. This method's results correctly detect most of the corrupt and non-corrupt contracts evaluated in the dataset. Furthermore, we found that the most critical predictors considered in the model are those related to the relationship between buyers and suppliers rather than those related to features of individual contracts. Also, the method proposed here is general enough to be trained with data from other countries. Overall, our work presents a tool that can help in the decision-making process to identify, predict and analyze corruption in public procurement contracts.

Yuri Nakayama, Yuka Takedomi, Towa Suda et al.

Vaccines are promising tools to control the spread of COVID-19. An effective vaccination campaign requires government policies and community engagement, sharing experiences for social support, and voicing concerns to vaccine safety and efficiency. The increasing use of online social platforms allows us to trace large-scale communication and infer public opinion in real-time. We collected more than 100 million vaccine-related tweets posted by 8 million users and used the Latent Dirichlet Allocation model to perform automated topic modeling of tweet texts during the vaccination campaign in Japan. We identified 15 topics grouped into 4 themes on Personal issue, Breaking news, Politics, and Conspiracy and humour. The evolution of the popularity of themes revealed a shift in public opinion, initially sharing the attention over personal issues (individual aspect), collecting information from the news (knowledge acquisition), and government criticisms, towards personal experiences once confidence in the vaccination campaign was established. An interrupted time series regression analysis showed that the Tokyo Olympic Games affected public opinion more than other critical events but not the course of the vaccination. Public opinion on politics was significantly affected by various events, positively shifting the attention in the early stages of the vaccination campaign and negatively later. Tweets about personal issues were mostly retweeted when the vaccination reached the younger population. The associations between the vaccination campaign stages and tweet themes suggest that the public engagement in the social platform contributed to speedup vaccine uptake by reducing anxiety via social learning and support.

Ravishankar Ramanathan, Michał Banacki, Paweł Horodecki

The extraction of randomness from weakly random seeds is a topic of central importance in cryptography. Weak sources of randomness can be considered to be either private or public, where public sources such as the NIST randomness beacon broadcast the random bits once they are generated. The problem of device-independent randomness extraction from weak public sources against no-signalling adversaries has remained open. In this paper, we show protocols for device-independent and one-sided device-independent amplification of randomness from weak public Santha Vazirani (SV) sources that use a finite number of devices and are secure against no-signaling adversaries. Specifically, under the assumption that the device behavior is as prescribed by quantum mechanics the protocols allow for amplification of public $ε$-SV sources for arbitrary initial $ε\in [0,0.5)$. On the other hand, when only the assumption of no-signaling between the components of the device is made, the protocols allow for amplification of a limited set of weak public SV sources.

Vishwali Mhasawade, Yuan Zhao, Rumi Chunara

Research in population and public health focuses on the mechanisms between different cultural, social, and environmental factors and their effect on the health, of not just individuals, but communities as a whole. We present here a very brief introduction into research in these fields, as well as connections to existing machine learning work to help activate the machine learning community on such topics and highlight specific opportunities where machine learning, public and population health may synergize to better achieve health equity.