Hasil untuk "Recreation leadership. Administration of recreation services"

Nicholas Caputo

Advanced AI systems are now being used in AI governance. Practitioners will likely delegate an increasing number of tasks to them as they improve and governance becomes harder. However, using AI for governance risks serious harms because human practitioners may not be able to understand AI decisions or determine whether they are aligned to the user's interests. Delegation may also undermine governance's legitimacy. This paper begins to develop a principled framework for when to delegate AI governance to AIs and when (and how) to maintain human participation. Administrative law, which governs agencies that are (1) more expert in their domains than the legislatures that create them and the courts that oversee them and (2) potentially misaligned to their original goals, offers useful lessons. Administrative law doctrine provides examples of clear, articulated rules for when delegation can occur, what delegation can consist of, and what processes can keep agencies aligned even as they are empowered to achieve their goals. The lessons of administrative law provide a foundation for how AI governance can use AI in a safe, accountable, and effective way.

Catalin Vrabie

Integration of Machine Learning (ML) techniques into public administration marks a new and transformative era for e-government systems. While traditionally e-government studies were focusing on text-based interactions, this one explores the innovative application of ML for image analysis, an approach that enables governments to address citizen petitions more efficiently. By using image classification and object detection algorithms, the model proposed in this article supports public institutions in identifying and fast responding to evidence submitted by citizens in picture format, such as infrastructure issues, environmental concerns or other urban issues that citizens might face. The research also highlights the Jevons Paradox as a critical factor, wherein increased efficiency from the citizen side (especially using mobile platforms and apps) may generate higher demand which should lead to scalable and robust solutions. Using as a case study a Romanian municipality who provided datasets of citizen-submitted images, the author analysed and proved that ML can improve accuracy and responsiveness of public institutions. The findings suggest that adopting ML for e-petition systems can not only enhance citizen participation but also speeding up administrative processes, paving the way for more transparent and effective governance. This study contributes to the discourse on e-government 3.0 by showing the potential of Artificial Intelligence (AI) to transform public service delivery, ensuring sustainable (and scalable) solutions for the growing demands of modern urban governance.

Jie Chen, Xianbin Wang

Conventional mobile networks, including both localized cell-centric and cooperative cell-free networks (CCN/CFN), are built upon rigid network topologies. However, neither architecture is adequate to flexibly support distributed integrated sensing and communication (ISAC) services, due to the increasing difficulty of aligning spatiotemporally distributed heterogeneous service demands with available radio resources. In this paper, we propose an elastic network topology (ENT) for distributed ISAC service provisioning, where multiple co-existing localized CCNs can be dynamically aggregated into CFNs with expanded boundaries for federated network operation. This topology elastically orchestrates localized CCN and federated CFN boundaries to balance signaling overhead and distributed resource utilization, thereby enabling efficient ISAC service provisioning. A two-phase operation protocol is then developed. In Phase I, each CCN autonomously classifies ISAC services as either local or federated and partitions its resources into dedicated and shared segments. In Phase II, each CCN employs its dedicated resources for local ISAC services, while the aggregated CFN consolidates shared resources from its constituent CCNs to cooperatively deliver federated services. Furthermore, we design a utility-to-signaling ratio (USR) to quantify the tradeoff between sensing/communication utility and signaling overhead. Consequently, a USR maximization problem is formulated by jointly optimizing the network topology (i.e., service classification and CCN aggregation) and the allocation of dedicated and shared resources. However, this problem is challenging due to its distributed optimization nature and the absence of complete channel state information. To address this problem efficiently, we propose a multi-agent deep reinforcement learning (MADRL) framework with centralized training and decentralized execution.

Thomas Compton

This paper presents a comparative analysis of community unionism (CU) in two distinct historical and organizational contexts: the National Boot and Shoe Union (B\&S) in the 1920s and Unite Community in the 2010s--2020s. Using BERTopic for thematic modeling and cTF-IDF weighting, alongside word frequency analysis, the study examines the extent to which each union's discourse aligns with key features of CU -- such as coalition-building, grassroots engagement, and action beyond the workplace. The results reveal significant differences in thematic focus and discursive coherence. While Unite Community demonstrates stronger alignment with outward-facing, social justice-oriented themes, the B\&S corpus emphasizes internal administration, industrial relations, and member services -- reflecting a more traditional, servicing-oriented union model. The analysis also highlights methodological insights, demonstrating how modern NLP techniques can enhance the study of historical labor archives. Ultimately, the findings suggest that while both unions engage with community-related themes, their underlying models of engagement diverge significantly, challenging assumptions about the continuity and universality of community unionism across time and sector.

Yining Liu, Yanfeng Ouyang

Dockless electric micro-mobility services (e.g., shared e-scooters and e-bikes) have been increasingly popular in the recent decade, and a variety of charging technologies have emerged for these services. The use of charging stations, to/from which service vehicles are transported by the riders for charging, poses as a promising approach because it reduces the need for dedicated staff or contractors. However, unique challenges also arise, such as how to incentivize riders to drop off vehicles at stations and how to efficiently utilize the vehicles being charged at the stations. This paper focuses on dockless e-scooters as an example and develops a new spatial queuing network model to capture the steady-state scooter service cycles, battery consumption and charging processes, and the associated pricing and management mechanisms. Building upon this model, a system of closed-form equations is formulated and incorporated into a constrained nonlinear program to optimize the deployment of the service fleet, the design of charging stations (i.e., number, location, and capacity), user-based charging price promotions and priorities, and repositioning truck operations (i.e., headway and truck load). The proposed queuing network model is found to match very well with agent-based simulations. It is applied to a series of numerical experiments to draw insights into the optimal designs and the system performance. The numerical results reveal strong advantages of using charging stations for shared dockless electric micro-mobility services as compared to state-of-the-art alternatives. The proposed model can also be used to analyze other micromobility services and other charging approaches.

Paolo Testolina, Michele Polese, Tommaso Melodia

The upper midband, spanning 7 to 24 GHz, strikes a good balance between large bandwidths and favorable propagation environments for future 6th Generation (6G) networks. Wireless networks in the upper midband, however, will need to share the spectrum and safely coexist with a variety of incumbents, ranging from radiolocation to fixed satellite services, as well as Earth exploration and sensing. In this paper, we take the first step toward understanding the potential and challenges associated with cellular systems between 7 and 24 GHz. Our focus is on the enabling technologies and policies for coexistence with established incumbents. We consider dynamic spectrum sharing solutions enabled by programmable and adaptive cellular networks, but also the possibility of leveraging the cellular infrastructure for incumbent services. Our comprehensive analysis employs ray tracing and examines real-world urban scenarios to evaluate throughput, coverage tradeoffs, and the potential impact on incumbent services. Our findings highlight the advantages of FR-3 over FR-2 and FR-1 in terms of coverage and bandwidth, respectively. We conclude by discussing a network architecture based on Open RAN, aimed at enabling dynamic spectrum and service sharing.

T. Taleb, D. E. Bensalem, A. Laghrissi

The fifth generation (5G) and beyond wireless networks are foreseen to operate in a fully automated manner, in order to fulfill the promise of ultra-short latency, meet the exponentially increasing resource requirements, and offer the quality of experience (QoE) expected from end-users. Among the ingredients involved in such environments, network slicing enables the creation of logical networks tailored to support specific application demands (i.e., service level agreement SLA, quality of service QoS, etc.) on top of physical infrastructure. This creates the need for mechanisms that can collect spatiotemporal information on users'service consumption, and identify meaningful insights and patterns, leveraging machinelearning techniques. In this vein, our paper proposes a framework dubbed"SOCLfor" the Service Oriented CLustering, analysis and profiling of users (i.e., humans, sensors, etc.) when consuming enhanced Mobile BroadBand (eMBB) applications, internet of things (IoT) services, and unmanned aerial vehicles services (UAVs). SOCL relies mainly on the realistic network simulation framework"network slice planne"(NSP), and two clustering methods namely K-means and hierarchical clustering. The obtained results showcase interesting features, highlighting the benefit of the proposed framework.

Mark Reid, James Montgomery, Barry Drake et al.

This document presents the draft specification for delivering machine learning services over HTTP, developed as part of the Protocols and Structures for Inference project, which concluded in 2013. It presents the motivation for providing machine learning as a service, followed by a description of the essential and optional components of such a service.

Hridoy Sankar Dutta, Kartik Aggarwal, Tanmoy Chakraborty

The popularity of Twitter has fostered the emergence of various fraudulent user activities - one such activity is to artificially bolster the social reputation of Twitter profiles by gaining a large number of followers within a short time span. Many users want to gain followers to increase the visibility and reach of their profiles to wide audiences. This has provoked several blackmarket services to garner huge attention by providing artificial followers via the network of agreeable and compromised accounts in a collusive manner. Their activity is difficult to detect as the blackmarket services shape their behavior in such a way that users who are part of these services disguise themselves as genuine users. In this paper, we propose DECIFE, a framework to detect collusive users involved in producing 'following' activities through blackmarket services with the intention to gain collusive followers in return. We first construct a heterogeneous user-tweet-topic network to leverage the follower/followee relationships and linguistic properties of a user. The heterogeneous network is then decomposed to form four different subgraphs that capture the semantic relations between the users. An attention-based subgraph aggregation network is proposed to learn and combine the node representations from each subgraph. The combined representation is finally passed on to a hypersphere learning objective to detect collusive users. Comprehensive experiments on our curated dataset are conducted to validate the effectiveness of DECIFE by comparing it with other state-of-the-art approaches. To our knowledge, this is the first attempt to detect collusive users involved in blackmarket 'following services' on Twitter.

Yujie Wamg, Xin Du, Xuzhao Chen et al.

Artificial intelligence is one of the important technologies for industrial applications, but it requires a lot of computing resources and sensor data to support it. With the development of edge computing and the Internet of Things, artificial intelligence are playing an increasingly important role in the field of edge services. Therefore, how to make intelligent algorithms provide better services and the development of the Internet of Things has become an increasingly important topic. This paper focuses on the application of edge service distribution strategy, and proposes an edge service distribution strategy based on intelligent prediction, which reduces the bandwidth consumption of edge service providers and minimizes the cost of edge service providers. In addition, this article uses the real data provided by the Wangsu Technology Company and an improved long and short term memory prediction method to dynamically change the bandwidth, and achieves better optimization of resources allocation comparing with actual industrial applications.The simulation results show that our intelligent prediction can achieve good results, and the mechanism can achieve higher resource utilization.

Xiao Xue, ZhiYong Feng, ShiZhan Chen et al.

Intelligence services are playing an increasingly important role in the operation of our society. Exploring the evolution mechanism, boundaries and challenges of service ecosystem is essential to our ability to realize smart society, reap its benefits and prevent potential risks. We argue that this necessitates a broad scientific research agenda to study service ecosystem that incorporates and expands upon the disciplines of computer science and includes insights from across the sciences. We firstly outline a set of research issues that are fundamental to this emerging field, and then explores the technical, social, legal and institutional challenges on the study of service ecosystem.

Yijie Li, Jing Chen

Ecosystem services refer to the ones human beings often obtain from the natural environment ecosystem. In order to solve the problem of environmental degradation, based on the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST model), this paper makes innovation by adding the urban module that was not in the previous models, which can better deal with the evaluation of ecosystem services in urban scenarios.

Matthew Arnold, Rachel K. E. Bellamy, Michael Hind et al.

Accuracy is an important concern for suppliers of artificial intelligence (AI) services, but considerations beyond accuracy, such as safety (which includes fairness and explainability), security, and provenance, are also critical elements to engender consumers' trust in a service. Many industries use transparent, standardized, but often not legally required documents called supplier's declarations of conformity (SDoCs) to describe the lineage of a product along with the safety and performance testing it has undergone. SDoCs may be considered multi-dimensional fact sheets that capture and quantify various aspects of the product and its development to make it worthy of consumers' trust. Inspired by this practice, we propose FactSheets to help increase trust in AI services. We envision such documents to contain purpose, performance, safety, security, and provenance information to be completed by AI service providers for examination by consumers. We suggest a comprehensive set of declaration items tailored to AI and provide examples for two fictitious AI services in the appendix of the paper.

Suproteem K. Sarkar, Kojin Oshiba, Daniel Giebisch et al.

Algorithms are increasingly common components of high-impact decision-making, and a growing body of literature on adversarial examples in laboratory settings indicates that standard machine learning models are not robust. This suggests that real-world systems are also susceptible to manipulation or misclassification, which especially poses a challenge to machine learning models used in financial services. We use the loan grade classification problem to explore how machine learning models are sensitive to small changes in user-reported data, using adversarial attacks documented in the literature and an original, domain-specific attack. Our work shows that a robust optimization algorithm can build models for financial services that are resistant to misclassification on perturbations. To the best of our knowledge, this is the first study of adversarial attacks and defenses for deep learning in financial services.

Shubhadip Mitra, Priya Saraf, Arnab Bhattacharya

Facility location problems aim to identify the best locations to set up new services. Majority of the existing works typically assume that the users are static. However, there exists a wide array of services such as fuel stations, ATMs, food joints, etc., that are widely accessed by mobile users besides the static ones. Such trajectory-aware services should, therefore, factor in the trajectories of its users rather than simply their static locations. In this work, we introduce the problem of optimal placement of facility locations for such trajectory-aware services that minimize the user inconvenience. The inconvenience of a user is the extra distance traveled by her from her regular path to avail a service. We call this the TIPS problem (Trajectory-aware Inconvenience-minimizing Placement of Services) and consider two variants of it. The goal of the first variant, MAXTIPS, is to minimize the maximum inconvenience faced by any user, while that of the second, AVGTIPS, is to minimize the average inconvenience over all the users. We show that both these problems are NP-hard, and propose multiple efficient heuristics to solve them. Empirical evaluation on real urban-scale road networks validate the efficiency and effectiveness of the proposed heuristics.

Peter Nemeth

A quick look at research and development in astronomy shows that we live in exciting times. Exoplanetary systems, supernovae, and merging binary black holes were far out of reach for observers two decades ago and now such phenomena are recorded routinely. This quick development would not have been possible without the ability for researchers to be connected, to think globally and to be mobile. Classical short-term positions are not always suitable to support these conditions and freelancing may be a viable alternative. We introduce the Astroserver framework, which is a new freelancing platform for scientists, and demonstrate through examples how it contributed to some recent projects related to hot subdwarf stars and binaries. These contributions, which included spectroscopic data mining, computing services and observing services, as well as artwork, allowed a deeper look into the investigated systems. The work on composite spectra binaries provided new details for the hypervelocity wide subdwarf binary PB 3877 and found diverse and rare systems with sub-giant companions in high-resolution spectroscopic surveys. The models for the peculiar abundance pattern of the evolved compact star LP 40-365 showed it to be a bound hypervelocity remnant of a supernova Iax event. Some of these works also included data visualizations to help presenting the new results. Such services may be of interest for many researchers.

Huan Feng, Kang G. Shin

In Android, communications between apps and system services are supported by a transaction-based Inter-Process Communication (IPC) mechanism. Binder, as the cornerstone of this IPC mechanism, separates two communicating parties as client and server. As with any client-server model, the server should not make any assumption on the validity (sanity) of client-side transaction. To our surprise, we find this principle has frequently been overlooked in the implementation of Android system services. In this paper, we demonstrate the prevalence and severity of this vulnerability surface and try to answer why developers keep making this seemingly simple mistake. Specifically, we design and implement BinderCracker, an automatic testing framework that supports parameter-aware fuzzing and has identified more than 100 vulnerabilities in six major versions of Android, including the latest version Android 6.0, Marshmallow. Some of the vulnerabilities have severe security implications, causing privileged code execution or permanent Denial-of-Service (DoS). We analyzed the root causes of these vulnerabilities to find that most of them exist because system service developers only considered exploitations via public APIs. We thus highlight the deficiency of testing only on client-side public APIs and argue for the necessity of testing and protection on the Binder interface - the actual security boundary. Specifically, we discuss the effectiveness and practicality of potential countermeasures, such as precautionary testing and runtime diagnostic.

Badrunnesa Zaman, Darshana Sedera

There is a considerable amount of awareness of environmental issues and corporate responsibility for sustainability. As such, from a technological viewpoint, Green IT has become an important topic in contemporary organizations. Consequently, organisations are expected to be innovative in their business practices to become more sustainable. Yet, the popularity and adoption of such initiatives amongst employees remain low. Furthermore, the management practices for adhering to Green IT are largely dormant, lacking active incentives for employees to engage in Green IT initiatives. This study observes the phenomenon of Green IT through administrative innovation. In doing so this paper performs a comprehensive analysis of 137 papers published between 2007 and 2015. The paper reveals organizational factors for successful implementation of Green IT as administrative innovation that can be useful to both academia and practice.

Charles Sutton, Michael I. Jordan

Modern Internet services, such as those at Google, Yahoo!, and Amazon, handle billions of requests per day on clusters of thousands of computers. Because these services operate under strict performance requirements, a statistical understanding of their performance is of great practical interest. Such services are modeled by networks of queues, where each queue models one of the computers in the system. A key challenge is that the data are incomplete, because recording detailed information about every request to a heavily used system can require unacceptable overhead. In this paper we develop a Bayesian perspective on queueing models in which the arrival and departure times that are not observed are treated as latent variables. Underlying this viewpoint is the observation that a queueing model defines a deterministic transformation between the data and a set of independent variables called the service times. With this viewpoint in hand, we sample from the posterior distribution over missing data and model parameters using Markov chain Monte Carlo. We evaluate our framework on data from a benchmark Web application. We also present a simple technique for selection among nested queueing models. We are unaware of any previous work that considers inference in networks of queues in the presence of missing data.

M. Mrissa, Ph. Thiran, J-M. Jacquet et al.

Data exchanges between Web services engaged in a composition raise several heterogeneities. In this paper, we address the problem of data cardinality heterogeneity in a composition. Firstly, we build a theoretical framework to describe different aspects of Web services that relate to data cardinality, and secondly, we solve this problem by developing a solution for cardinality mediation based on constraint logic programming.