Service Requests Research Articles

Two Approaches to the Traffic Quality Intuitionistic Fuzzy Estimation of Service Compositions

Recently, intuitionistic fuzzy pairs have been used as uncertainty estimations of the request services in service systems. In the present paper, three intuitionistic fuzzy characterizations of virtual service devices are specified: intuitionistic fuzzy traffic estimation, intuitionistic fuzzy flow estimation and intuitionistic fuzzy estimation about probability. Discussed herein are two approaches to the intuitionistic fuzzy estimation of the uncertainty of compositions of services. One of the approaches is based on the definitions of the intuitionistic fuzzy pairs for one service device. The other approach is based on the aggregation operators over intuitionistic fuzzy pairs. A total of six intuitionistic fuzzy estimations of the uncertainty of comprise service device are proposed. The proposed uncertainty estimations allow for the definition of new Quality of Service (QoS) indicators and can be used to determine the quality of service compositions across a wide range of service systems.

Blockchain-Based Trust and Reputation Management in SIoT

In the Social Internet of Things (SIoT), trust refers to the decision-making process used by the trustor (Service Requesters (SRs) or Service Consumers (SCs)) to decide whether or not to entrust the trustee (Service Providers (SPs)) with specific services. Trust is the key factor in SIoT domain. The designing of a two-way, two-stage parameterized feedback-based, service-driven, attacks-resistant trust and reputation system for SIoT accompanied by a penalty mechanism for dishonest SPs and SRs is our main contribution that mitigates the trust-related issues occurring during service provisioning and service acquisition amongst various entities (SPs or SRs) and enhances trust amongst them. Our proposed methodology examines a SP’s local trust, global trust, and reputation by taking into account “Social Trust” and “Quality of Service (QoS)” factors”. Two—Stage Parameterized feedback” is incorporated in our proposed strategy to better manage “intention” and “ability” of SRs and provides early identification of suspicious SRs. This feature compels SRs to act honestly and rate the corresponding SPs in a more accurate way. Our recommended paradigm sorts SPs into three SP status lists (White List, Grey List, and Black List) based on reputation values where each list has a threshold with respect to the maximum service fee that can be charged. SPs in White List charge the most per service. SPs in other lists have a lower selection probability. Every feedback updates the SP’s trust and reputation value. Sorting SPs increases resistance against On Off Attack, Discriminatory Attack, Opportunistic Service Attack, and Selective Behavior Attacks. SPs must operate honestly and offer the complete scope of stated services since their reputation value relies on all their global trust values (Tglobal) for various services. Service requests may be accepted or denied by SPs. “Temporarily banned” SRs can only request unblocked services. SRs lose all privileges once on a “permanently banned” list. If local and global trust values differ by more than the threshold, the SR is banned. Our method also provides resistance against Bad Mouthing Attack, Ballot Stuffing Attack. Good Mouthing Attack/Self—Propagating Attack. Experiments indicate our trust and reputation management system recognizes and bans fraudulent SRs. “Dishonest SPs” are “blacklisted,” which affects their reputation, trust, and service charges.

NETWORK/SECURITY THREATS AND COUNTERMEASURES FOR CLOUD COMPUTING

Small and medium-sized enterprises are now aiming to adopt a cost-effective computing resource for their business applications, i.e. by using the novel idea of cloud computing in their environment, in addition to major corporations. Utilizing less resources and management support, a shared network, valuable resources, bandwidth, software, and hardware in a cost-effective manner, and fewer service provider interactions, cloud computing enhances the performance of companies. In essence, it’s a novel idea for giving consumers access to virtualized resources. Customers can use the cloud to store a lot of data from several locations and request services, applications, and solutions. But as cloud computing’s popularity grows, there is a growing danger that security will overtake other concerns as the primary one. The current article suggests a backup strategy needed to address cloud computing security concerns.

Optimal Management of Energy Communities Hosting a Fleet of Electric Vehicles

In this paper, we study the problem of managing an energy community hosting a fleet of electric vehicles for rent. On the day ahead, service requests for electric vehicles are submitted to the community. Then, the optimal request-to-vehicle assignment has to be found, as well as the optimal charging schedule of vehicle batteries. A suitable model is presented and included in an existing energy community architecture. The overall community management problem is formulated as a bi-level model, featuring two nested optimization problems. The optimal request-to-vehicle assignment requires the solution of a mixed-integer linear program. To reduce the computational complexity, a heuristic solution to the assignment problem is presented. Numerical results show that participation in the community grants a remarkable reduction in the electric vehicle charging cost. The adoption of the heuristic assignment solution provides a dramatic reduction in the computation time required to solve the bi-level model. At the same time, the level of suboptimality introduced appears to be negligible, being less than 1% in most of the considered cases.

Time Factor for E-KTP Service Through POS KETANMU at the Population and Civil Registration Office

The government of the Population and Civil Registration Service of the Mojokerto Regency in implementing E-KTP services through the online service without meeting systems (POS KETANMU) must have a predetermined goal or target. To support the realization of E-KTP services in accordance with standard operating procedures, it must have the right time factor so that this E-KTP services can be carried out quickly and precisely. This study aims to describe the condition of an object of research, namely the E-KTP service through POS KETANMU as it is and based on existing facts. This research method uses a qualitative method with a descriptive approach. The results of the study show that the time factor has provided services to the community and has not received a good response from the community. The public is enthusiastic to hear about the E-KTP service through this online system. However, the implementation process has not yet reached the predetermined time factor. This is due to the lack of orderly service providers in responding to request for public services.

The rise of chatbots: The effect of using chatbot agents on consumers' responses to request rejection

AbstractThis research investigates consumers’ perceptions and evaluations of robot service agents compared with human service agents when service requests are rejected. Six studies were conducted. The results show that when consumers receive a rejection of their service request, they evaluate the service less negatively if the service is handled by a chatbot agent versus a human agent. The reason is that consumers have lower expectations that robots will be able to provide flexible services to them. Consequently, their dissatisfaction with the request rejection is lower when the service is handled by robots. However, the aforementioned effect is not observed (1) when consumers have not experienced the service yet, (2) when their service request has been accepted, or (3) when the service agent conveys emotions to apologize for request rejection.

Online crowdsourced delivery for urban parcels using private cars under time-dependent travel times

This paper studies the problem of online crowdsourced delivery for urban parcels using private cars under time-dependent travel times. We consider some complex and practical cases, such as time-dependent travel times, private car detours and distribution, and retail stores as delivery or transfer nodes. The online delivery problem is formulated as a mixed-integer programming model to maximize the total profit. For this, we design an improved adaptive large neighbourhood search to address the problem. The benchmark instances verify the effectiveness and applicability of the improved ALNS. Then, the instances of large-scale delivery network using the main urban area of Dalian as an actual scenario are experimented. Numerical experiments showed that well-planned online crowdsourced delivery can respond to service requests in a shorter time, and the profit is higher than FlashEx. Sensitivity analysis results show that the maximum detour coefficient is only increased within a certain range, which is helpful to improve the matching rate and total profit. Moreover, increasing the number of signed private cars can not only increase the direct delivery rate but also the matching rate and total profit.

Dynamic container drayage with uncertain request arrival times and service time windows

Container drayage plays a critical role in intermodal global container transportation, as it accomplishes the first- and last-mile shipment of containers. A container drayage operator dispatches a set of tractors and a set of trailers to transport containers within a local area. An important aspect of the operations is that the arrival times of service requests are uncertain, which means that the operator should respond to the requests dynamically. Moreover, since customers usually impose time windows on container pickup and delivery, it would be important to exploit the service flexibilities of requests when allocating resources in order to enhance the resource efficiency. In this paper, we study a dynamic container drayage problem that arises from the practical operations of container drayage. We develop a Markov decision process (MDP) model for the problem to capture the dynamic interactions between the drayage operator and the uncertain environment. For solving the MDP model, we propose a novel integrated reinforcement learning and integer programming method, in which reinforcement learning enables real-time responses to requests by determining whether each request should be served immediately upon arrival or be held for a period of time, while integer programming plans resource allocation periodically for serving the accrued requests. The proposed method aims to identify a fleet management policy that exploits requests’ service flexibilities to maximize the operator’s service capacity and profitability. We also evaluate the performance of the proposed method on instances generated from the operational data of a container drayage operator in Singapore.

Manufacturing operations as services by robots with skills

Abstract This work considers flexible manufacturing operations based on reconfigurable robotic skills and their usage in fully automated service manufacturing. In agile and ultra-flexible manufacturing operations, where lot sizes go down to one, the setup and execution of new tasks must be instant. We extend service manufacturing towards applications of multi-purpose autonomous mobile robots. We take digital data and service-oriented approach to configure and utilize re-usable robot operations formulated as robot skills. We integrate service requests, and system and robot skill models for an easily executable manufacturing service system. We show the feasibility of our approach by experimental tests with merged indoor logistics, assembly, and finishing tasks.

A Natural Language Processing and deep learning based model for automated vehicle diagnostics using free-text customer service reports

Initial fault detection and diagnostics are imperative measures to improve the efficiency, safety, and stability of vehicle operation. In recent years, numerous studies have investigated data-driven approaches to improve the vehicle diagnostics process using available vehicle data. Moreover, data-driven methods are employed to enhance customer-service agent interactions. In this study, we demonstrate a machine learning pipeline to improve automated vehicle diagnostics. First, Natural Language Processing (NLP) is used to automate the extraction of crucial information from free-text failure reports (generated during customers’ calls to the service department). Then, deep learning algorithms are employed to validate service requests and filter vague or misleading claims. Ultimately, different classification algorithms are implemented to classify service requests so that valid service requests can be directed to the relevant service department. The proposed model – Bidirectional Long Short Term Memory (BiLSTM) along with Convolution Neural Network (CNN) – shows more than 18% accuracy improvement in validating service requests compared to technicians’ capabilities. In addition, using domain-based NLP techniques at preprocessing and feature extraction stages along with CNN-BiLSTM based request validation enhanced the accuracy (>25%), sensitivity (>39%), specificity (>11%), and precision (>11%) of Gradient Tree Boosting (GTB) service classification model. The Receiver Operating Characteristic Area Under the Curve (ROC-AUC) reached 0.82.

Cloud service selection based on weighted KD tree nearest neighbor search

The military organization cloud cooperation is a new type of military organization that encapsulates combat resources into services and makes them available as a service. Because of the heterogeneity of the combat resources, the cloud services are different not only in functional properties, but also in non-functional properties. Hence, the idea of cloud service selection, which aims at providing service requester with cloud services that meet their needs for non-functional attributes, has emerged. To this end, this paper proposes a cloud service selection method based on the weighted KD tree nearest neighbor search (WKDTNNS) algorithm for the personalized search of cloud services with different weights in each dimension. Firstly, for the atomic cloud service selection, the KD tree of all atomic cloud services with the quality of service (QoS) as the key is constructed, and WKDTNNS is used to match cloud services for service requests from users. Then, for combined cloud service selection, the KD tree is constructed based on pre-established combined cloud services to select several similar services as seeds, and the WKDTNNS algorithm is applied to optimize the seeds and obtain the final results. Finally, a case study is presented to show the feasibility and effectiveness of the proposed method, the results show that the proposed method could achieve better results than other algorithms, with 26%–56% less computation time.

Edge intelligence for service function chain deployment in NFV-enabled networks

With evolution of network function virtualization (NFV), network services can be provided as service function chains (SCs), each consisting of multiple virtual network functions (VNFs). The deployment of SCs including placement of VNF instances and virtual links connecting these functions, onto the substrate physical network is a critical issue which significantly affects the performance of the offered network services. Due to the unpredictable traffic and network state variations, as well as diverse quality of service (QoS) requirements, an online SCs deployment approach is needed to cope with different service requests and real-time network traffics. In this paper, we employ edge intelligence using a distributed deep reinforcement learning approach to deploy SCs in order to jointly balance the load on the physical nodes and links in the edge environments. The evaluation results show that the proposed approach outperforms state-of-the-art algorithms in terms of minimizing the drop rate of the incoming service chain requests. In addition, the proposed approach is able to rapidly deploy service flows even in the large real-world network typologies.

Toward a Trust Evaluation Framework Against Malicious Behaviors of Industrial IoT

With the development of the Industrial Internet of Things (IIoT) technology, edge computing is a promising area to release the sensing and computing burdens from the overloaded center. However, in edge network scenarios, we cannot trust every node’s output since some nodes can behave maliciously by making use of the properties, such as multiple identities, heterogeneous capabilities, and mobility. In that case, trust management is widely used to solve the problem of network trustworthiness. In this article, we propose a trust evaluation framework by comprehensively considering the nodes’ malicious behaviors and heterogeneous characteristics of edge networks. Under the Bayesian framework, we use the semi-ring theory to dynamically establish mobile-edge nodes’ trust models. First, we calculate the trust value for each node with a different identity (service provider or requester). Then, we propose a security-regarded task allocation mechanism to improve the reliability of selected trusted nodes according to the matched relationship between the service requesters’ expected capability and the providers’ actual capability. Further, we conduct extensive analysis and simulations to evaluate the proposed methods in typical IIoT scenarios. The results show that the proposed method has better immunity to abnormal behaviors, including the noncooperation, malicious feedback, on–off attacks, Sybil attacks, whitewashing attack, malicious access, etc., and has higher scheduling accuracy and controllable time complexity compared to existing methods.

Request Scheduling Combined With Load Balancing in Mobile-Edge Computing

Edge servers can cache some delay-sensitive and resource-intensive applications to reduce the delay of user tasks. However, due to the limited resources of edge servers, they cannot cache all services and process all user requests like remote clouds. In order to take advantage of the low latency, we need to make full use of the limited resources of the edge servers and reasonably allocate user requests to edge servers for processing. Meanwhile, in order to maintain the efficient and long-term operation of the server cluster, we should also consider the load balancing of the cluster. How to balance the server load while making users have the best experience is an urgent problem to be solved. Solving the problem faces the challenge of the interaction between service placement and request scheduling, the tradeoff between communication and computation, and the consideration of response time and edge server load. We propose the service placement algorithm based on user visits and our request scheduling algorithm based on the simulated annealing algorithm. We verify the superiority of our algorithms in response time and server load balancing. The experimental results based on the real data sets prove our algorithm can cope with the actual situation and quickly converge to a favorable value.

Using Behavioral Insights to Market a Workplace Safety Program: Evidence From a Multi-Armed Experiment.

This paper describes how a multi-armed randomized experiment was used to test multiple variants of a behaviorally informed marketing strategy. In particular, we tested whether specific behavioral messages could be used to increase demand for a safety consultation service offered by the U.S. Occupational Safety and Health Administration. Our experiment used a partial factorial design with 19 study arms and a very large research sample-97,182 establishments-to test the impact of various message, formats, and delivery modes compared with an existing (not behaviorally informed) informational brochure and a no-marketing counterfactual. A secondary research goal was to predict the impact of the most successful marketing strategy (i.e., combination of message, format, and mode) so that OSHA would know what to anticipate if that strategy were implemented at scale. We used two related (but distinct) methods to address these two goals. Both begin with a common mixed (i.e., fixed and random effects) ANOVA model. We addressed the first research goal primarily from the fixed effects; we addressed the second research goal by calculating best linear unbiased predictions (BLUPs) from the full mixed model, where the BLUP involves "shrinkage" as in empirical Bayes (EB) approaches. Marketing via brochures was effective overall, nearly doubling the rate of requests for services. However, the behaviorally informed materials performed no better than OSHA's existing informational brochure. This study also highlights the conditions under which a factorial design can be used to efficiently address questions about which of several program variants are most effective.

Transformation of public space utilization by online motorcycle taxi

This study aims to see the transformation of the use of public space by online motorcycle taxi. The use of technology in the concept of online motorcycle taxi makes it different from earlier concept of conventional ojek. Therefore, online motorcycle taxi does not have a designated place to wait for service requests. It makes online motorcycle taxi to make use of pre-existing public spaces as their places to wait or seek for service requests. This case can be seen on one of the main roads in South Jakarta, named St. Prof. DR. Satrio, which is one of the roads included in the revitalization program by the DKI Jakarta Provincial Government to become a pedestrian public space. This research uses qualitative method with ethnographic approach which data collection process consisted of participatory observation and direct interviews with onsite online motorcycle taxi drivers. The results show the transformation in functional form, which was changed from a green open space as a road divider into pelican sidewalk and public recreation areas. However, the purpose of revitalization is not in line with real condition where the space is used on by certain community (online motorcycle taxi drivers) that do not have designated places.

Requirements for a Gastronomic Reusable Glass System in Germany

The entry into force of the Single-Use Plastics Act on 07/03/2021 and the future obligation for gastronomic establishments to use reusable packaging from 01/01/2023, will set the course for reducing single-use consumption in Germany. The current research literature primarily highlights reuse systems with plastic, but little is known about a reuse system with glass as a material. The requirements for such a reusable system in Germany have thus hardly been researched up to now. Therefore, the purpose of this paper is to work out the requirements for a gastronomic reusable glass system in Germany. A mixed-method approach was used. In a pre-study with 14 experts[i] individual product and services requests for the reusable glass system have been identified. Based on a survey among 502 guests in the gastronomic sector hypothesis generated by the literature and the pre-study have been tested. In a survey among 102 restaurateurs requests for the gastronomic reusable glass system haven been categorised using the Kano model. The research highlights the necessity of a high variety of sizes for food and drinks, the stability, the stackability, the easy cleaning, fracture safety, freedom from microplastic of reusable containers. In addition, great importance is attached to simple handling and the consideration of guests’ needs concerning the reusable glass system. Accessories such as reusable cutlery and reusable straws are to be offered as part of a reusable system. Another requirement was the automated reordering of reusable containers.

Black maternal health scholars on fire: Building a network for collaboration and activism.

Black maternal health scholars on fire: Building a network for collaboration and activism.

Analisis Permintaan Jasa Transportasi Funotrans Di Kota Bandar Lampung

This study aims to determine and analyze respondents' income variables, prices for FunoTrans services, prices for other services, and user satisfaction with the number of requests for FunoTrans transportation services in Bandar Lampung City. The research uses descriptive quantitative methods which are classified as survey research, namely research that takes samples from one population by using a questionnaire. The research method uses multiple linear regression analysis. The results of this study include; the price of online transportation services for motorbikes and cars using the FunoTrans application has a positive and significant effect; the price of other online transportation services for motorcycles and cars has a positive and significant effect; respondent's income has no significant effect; and user satisfaction have a significant effect on the demand for online transportation services in the FunoTrans application.

IMPROVING THE EFFICIENCY OF AUTOMATION THE SCALING OF MICROSERVICES IN THE KUBERNETES CONTAINERIZED APPLICATION MANAGEMENT SYSTEM

Automatic container scaling in Kubernetes plays an important role in handling incoming requests from web application users. This article analyzes the stages of container scaling, their initialization and subsequent software launch. In order to achieve low latency of user requests in the case of dynamic load, the analysis of the process of automatic scaling of containers and the factors that will affect the scaling process was carried out. Having a full understanding of the processes and mechanisms by which scaling takes place, it became possible to develop a method and strategy for cleaning obstacles that slow down the autoscaling process itself and at the same time preserve the necessary properties of the existing scaling. Acceleration of scaling of containers, which will directly affect the speed of web services, becomes possible precisely because of the elimination of the delay in automatic scaling of containers. The work considered scaling optimization using not only container pre-creation networks, but also the use of container images, which enable the sharing of linked libraries in memory and the extension of Kubernetes’ declarative configuration management approach for parallel creation of multiple container instances. Based on the obtained research results, a method for optimizing the automatic scaling of containerized applications by eliminating the delay during a cold start has been developed. This latency manifests itself in the case of microservice autoscaling, where Kubernetes is expected to scale containers horizontally by creating additional replicas to the required number to handle the required traffic from the outside. The delay caused by the autoscaler affects the processing time of the user’s web service requests.