Service Requests Research Articles

Patient Reported Outcome Measurement (PROM) under real-life conditions of non-curable cancer outpatients with the Integrated Palliative Outcome Scale (IPOS) and NCCN-Distress Thermometer – A mixed methods study

ObjectiveProspective cohort study to test the real-life feasibility of longitudinal patient-reported outcome measurement PROM (Integrated Palliative Outcome Scale IPOS, and NCCN Distress Thermometer DT) required for outpatients with non-curable lung or prostate cancer in comprehensive cancer centers. MethodsAssessment with paper-based IPOS and DT was observed for 15 months. We analyzed response to patients' distress (requests for supportive and palliative services) following PROM. Focus groups to comprehensively explore the user experience of patients, informal caregivers and health care professionals (HCP) supplemented the analysis. ResultsNinety-seven percent (125/129) of the patients received a questionnaire once, but quarterly assessment as recommended by certification committees was achieved only in 50% and 31% of prostate and lung cancer patients. Although both instruments were well accepted, only IPOS showed a high content validity, because some patients had difficulties in understanding the DT. Patients felt comfortable with completing the PROM, and HCP found PROM helped to structure the patient encounter. Due to organizational deficiencies in the handling of the instruments and operationalization of reactions to identified distress, the referrals to supportive and palliative services were rare. ConclusionTo facilitate consequences from PROM it should be a standardized intervention rather than assessment alone. InnovationThe patient perspective improves the implementation of PROM under real-life clinical conditions.

Securing Service Instantiation on Next-generation Networks

The Next Generation of Software Defined Networks (NG-SDN) enables a whole new set of possibilities for innovation in the data plane (e.g., computing, monitoring, load-balancing). In parallel, we have been witnessing the increasing adoption of virtualized services running in light but efficient containers, which brings forth the capability to flexibly deploy services at the network edge, while providing dynamic scaling and management. As a result, operators have tap in a lot of computational real-estate as part of the adoption of telco cloud concepts, as well as to support the foreseen edge and fog computing scenarios. However, exposing the infrastructure to third parties and realizing complex services over a multi-stakeholder environment raises challenges at different levels (e.g., security, reliability, SLA guarantees). Reconfiguring the data plane for flow monitoring and handling service requests directly on the data plane could address these challenges to support the joint communication and computation vision. This is where this paper contributes by proposing a secure and reliable system for dynamic instantiation of third-party services. A proof-of-concept prototype was developed using P4 and Kubernetes showcasing the feasibility of the proposed approach.

An ADO‐OFDM with integrated precoding, companding and optimized power allocation methods for high‐speed data transmission in visible light communication

AbstractVisible light communications (VLC) has received a lot of attention in recent studies because of its benefits over radio‐frequency (RF) communications. It is a short‐range optical wireless communication system that uses light‐emitting diodes (LEDs) as transmitters. Optical orthogonal frequency division multiplexing (OFDM) is an auspicious technology for VLC high‐speed data transfer. The use of OFDM in a VLC system raises the system's peak‐to‐average power ratio (PAPR). The intrinsic non‐linearity of LED is a key concern in an asymmetrically clipped DC‐biased optical OFDM (ADO‐OFDM) system due to its high PAPR. Also, conventional ADO‐OFDM modulation scheme cannot be used for accommodating different demands of services in downlink multiple access due to the restriction of direct current biased optical OFDM (DCO‐OFDM) and asymmetrically clipped optical (ACO‐OFDM) transmission on odd and even subcarriers. To tackle this issue, a modified ADO‐OFDM (MADO‐OFDM) is proposed that adjusts the numbers of subcarriers required for the transmission of ACO‐OFDM and DCO‐OFDM adaptively based on the requests of services in downlink multiple access. Also, the proposed MADO‐OFDM is integrated with a discrete Hartley Matrix transform (DisHMT) precoder and Generalized Piecewise Linear Compander (GPLD) to provide high‐speed data transmission with less PAPR. In addition, the power is allocated to the proposed MADO‐OFDM system optimally by maximizing the channel capacity based on the Aquila optimizer algorithm. The simulation results reveal that the suggested system's PAPR is reduced by 2.4 dB and 0.8 dB, respectively, compared to conventional ADO and hybrid ADO‐OFDM. It also confirms that the suggested generalized PLC can greatly increase BER without affecting the PAPR performance.

Cross-Modal Stream Transmission: Architecture, Strategy, and Technology

Since the multi-modal services, which elaborately integrate audio, video, and haptic streams, are able to substantially improve the users' immersive experience, they are gradually becoming the mainstream applications. However, the existing solutions fail to simultaneously satisfy the diverse transmission requests of multi-modal services in terms of lower latency, higher reliability, and higher throughput, especially in the resource-constrained and dynamic environment. To circumvent this dilemma, this work constructs a cross-modal stream transmission architecture by exploring the intra-modal redundancy and the inter-modal correlation. Specifically, we propose an adaptive stream scheduling strategy, which can not only substantially decrease the stream traffics, but also dramatically reduce the impact of the channel uncertainty and the haptic signals' stochastic arrival on the delay jitter and reliability. Moreover, through prioritizing the resource provision of haptic signals, we design a joint uplink and downlink resource allocation scheme based on the prediction model. In particular, the transmitter sends the predicted haptic signals via the allocated resource blocks in advance according to the optimal window size, so as to offset the latency and remain the symmetry during the bilateral haptic transmission. Meanwhile, we develop an audio and video stream transmission scheme in a hierarchical and reusing structure by integrating the power-domain NOMA with LMDC-FEC, which fully utilizes the remaining limited resources to maximize the robustness of audio-video transmission.

Lightweight Imitation Learning for Real-Time Cooperative Service Migration

Due to the revolution of communication technology, the rapidly increasing number of mobile devices in edge networks generates various real-time service requests, requiring a considerable volume of heterogeneous resources all the time. However, edge devices with limited resources cannot afford substantial learning cost, while migrating services requires heterogeneous resources, especially for dynamic networks. To address these issues, we first establish a cooperative service migration framework and formulate a bi-objective optimization problem to optimize service performance and cost. By analyzing the optimal migration ratio of service cooperative migration, we propose an offline expert policy based on global states to provide optimal expert demonstrations. To realize real-time service migration based on observable states, we design a lightweight online agent policy to imitate expert demonstrations and leverage meta update to accelerate the model transfer. Experimental results show that our algorithm is exceptional in training cost and accuracy, and has significant superiors in multiple metrics such as the service latency and payment under different workloads, compared to other representative algorithms.

Efficient service reconfiguration with partial virtual network function migration

Network Function Virtualization (NFV) decouples network functions from dedicated hardware devices into Virtual Network Functions (VNFs). These VNFs are chained in order as a Service Function Chain (SFC) to provision flexible and efficient services. When service requests dynamically increase, the intensive workloads often lead to node overloads and further impact the Quality of Service (QoS). Existing works address this problem by migrating VNFs from overload nodes to other low-load nodes, known as VNF migration. However, when a VNF is shared by multiple SFCs, migrating the VNF will change the mapping relationships between these SFCs and the physical network (nodes and links). That may make some SFCs traverse more links and increase their propagation latency. That violates the demand of users for low-latency services. In this paper, to minimize the impact of VNF migration on SFC latency, we propose partial VNF migration. It migrates only partial VNFs within these SFCs to minimize the overall SFC latency while reducing migration costs. As such, we leverage partial VNF migration for efficient latency minimization with the formulation of an integer linear programming (ILP) model. Given the NP-hard nature of the problem, we propose a dynamic latency-aware partial VNF migration algorithm to reduce node overloads and minimize SFC latency. Evaluation indicates that the proposed approach has 12.7%–21.8% lower average SFC latency and 12.5%–48.5% less migration cost than state-of-the-art VNF migration algorithms. And it demonstrates about 90% shorter execution time with similar minimization performance, compared to other SFC reconfiguration algorithms.

The stochastic share-a-ride problem with electric vehicles and customer priorities

We introduce a stochastic share-a-ride problem in which a fleet of electric vehicles (EV) in a ride-hailing system are dynamically dispatched to serve passenger and parcel orders in a shared manner. We assume uncertain demands of both passenger and parcel orders and consider that passenger orders have priority over parcel orders. Passengers must be transported directly from their origins to destinations, while parcels can share a vehicle with other orders. The operator of the ride-hailing platform needs to decide whether to accept a newly arrived service request, how to assign orders to vehicles, and how to route and charge the EVs. To develop dynamic policies for the problem, we formulate the problem as a Markov decision process (MDP) and propose a reinforcement learning (RL) approach to solve the problem. We develop action-space restriction and state-space aggregation schemes to facilitate the implementation of the RL algorithm. We also present two rolling horizon heuristic methods to develop dynamic policies for our problem. We conduct computational experiments based on real-world taxi data from New York City. The computational results show that our RL policies perform better than the three benchmark policies in terms of serving more orders and collecting more rewards. Our RL policies are able to make high-quality decisions more efficiently when compared with the rolling horizon policies.

Implementation Of Public Service Digitization Policy In Gubeng Urban Village, Gubeng Sub-District Surabaya City (Study Of Surabaya Single Window (Ssw) Alfa Imlementation In Providing Public Services)

Surabaya Single Windows (SSW) Alfa is an innovation program for licensing and non-licensing services from the Surabaya City Government. Surabaya Single Windows Alfa is a development application from a previous application called Surabaya Single Windows which was launched in 2013. With the SSW Alfa application, residents of the city of Surabaya can process service requests online. Gubeng Village consistently serves residents by using the digitalization system provided by the Surabaya City Government. The aim of this research is to describe the implementation of the Surabaya Single Windows Alfa digitalization service program in Gubeng Village, Gubeng District, Surabaya City based on the theory of Successful Program Implementation according to George Edward III which consists of Communication, Disposition, Resources and Bureaucratic Structure. The method used in this research is descriptive qualitative by collecting observation and interview data. The research results show that the Surabaya Single Windows Alfa program has been running well. However, in its implementation there are still several obstacles that hinder the implementation of the program. The inhibiting factors found in this research are the need for equal understanding of the application of Surabaya Single Windows Alfa for human resources in Gubeng Village and the lack of socialization to the community regarding the Surabaya Single Windows Alfa program. This results in many people still not understanding the terms or documents required for the application.

Service Function Chain Deployment Algorithm Based on Deep Reinforcement Learning in Space–Air–Ground Integrated Network

SAGIN is formed by the fusion of ground networks and aircraft networks. It breaks through the limitation of communication, which cannot cover the whole world, bringing new opportunities for network communication in remote areas. However, many heterogeneous devices in SAGIN pose significant challenges in terms of end-to-end resource management, and the limited regional heterogeneous resources also threaten the QoS for users. In this regard, this paper proposes a hierarchical resource management structure for SAGIN, named SAGIN-MEC, based on a SDN, NFV, and MEC, aiming to facilitate the systematic management of heterogeneous network resources. Furthermore, to minimize the operator deployment costs while ensuring the QoS, this paper formulates a resource scheduling optimization model tailored to SAGIN scenarios to minimize energy consumption. Additionally, we propose a deployment algorithm, named DRL-G, which is based on heuristics and DRL, aiming to allocate heterogeneous network resources within SAGIN effectively. Experimental results showed that SAGIN-MEC can reduce the end-to-end delay by 6–15 ms compared to the terrestrial edge network, and compared to other algorithms, the DRL-G algorithm can improve the service request reception rate by up to 20%. In terms of energy consumption, it reduces the average energy consumption by 4.4% compared to the PG algorithm.

SLA-ORECS: an SLA-oriented framework for reallocating resources in edge-cloud systems

The emergence of the Fifth Generation (5G) era has ushered in a new era of diverse business scenarios, primarily characterized by data-intensive and latency-sensitive applications. Edge computing technology integrates the information services environment with cloud computing capabilities at the edge of the network. However, the evolving landscape of business models necessitates a unified edge architecture capable of accommodating diverse requirements, posing substantial challenges for service providers in meeting Service-Level Agreements (SLAs).In response to these challenges, we introduce SLA-ORECS. This innovative framework dynamically allocates dedicated and shared resources within the edge-cloud system to cater to service requests with varying SLAs, thereby facilitating performance isolation. Furthermore, we have developed an optimization algorithm to enhance the efficiency of SLA assurance during request dispatch.The evaluation of SLA-ORECS highlights its noteworthy performance improvements, particularly in terms of system throughput and average time consumption.

Analisis Kualitas Layanan Teknologi Informasi pada Domain Service Operation dengan Pendekatan Framework ITIL V.3 (Studi Kasus: Website Kampus Merdeka)

The Independent Campus programme, which allows students to take courses outside their programme of study and participate in activities outside the university, has experienced rapid growth. However, this growth brings a number of challenges, such as increased data volume, high service demands, and IT infrastructure complexity. The performance and responsiveness of IT services is also a concern, given users' expectations for fast performance. Effective customer support is also required to ensure a satisfactory user experience. To address these issues, an analysis of information technology (IT) management using the ITIL V3 framework in the Service Operation domain was conducted. The results of the analysis show that incident management, service requests, problems, access, and service operations can be improved through the implementation of ITIL. It helps improve operational efficiency, reduce the impact of incidents and problems, accelerate request fulfilment, maintain access security, and improve the quality and availability of IT services. The implementation of ITIL V3 is an important step in supporting the success of the Kampus Merdeka Programme in providing better access to education for students in Indonesia. Thus, the implementation of ITIL V3 is an important step in supporting the success of the Merdeka Campus Programme in providing better access to education for students in Indonesia.

QoS-Driven Slicing Management for Vehicular Communications

Network slicing is introduced for elastically instantiating logical network infrastructure isolation to support different application types with diversified quality of service (QoS) class indicators. In particular, vehicular communications are a trending area that consists of massive mission-critical applications in the range of safety-critical, intelligent transport systems, and on-board infotainment. Slicing management can be achieved if the network infrastructure has computing sufficiency, a dynamic control policy, elastic resource virtualization, and cross-tier orchestration. To support the functionality of slicing management, incorporating core network infrastructure with deep learning and reinforcement learning has become a hot topic for researchers and practitioners in analyzing vehicular traffic/resource patterns before orchestrating the steering policies. In this paper, we propose QoS-driven management by considering (edge) resource block utilization, scheduling, and slice instantiation in a three-tier resource placement, namely, small base stations/access points, macro base stations, and core networks. The proposed scheme integrates recurrent neural networks to trigger hidden states of resource availability and predict the output of QoS. The intelligent agent and slice controller, namely, RDQ3N, gathers the resource states from three-tier observations and optimizes the action on allocation and scheduling algorithms. Experiments are conducted on both physical and virtual representational vehicle-to-everything (V2X) environments; furthermore, service requests are set to massive thresholds for rendering V2X congestion flow entries.

An adaptive routing strategy in P2P-based Edge Cloud

P2P-based Edge Cloud (PEC) is widely used in Internet of Things (IoT). Inevitably, the sensor data routing technology has a significant impact on the performance of PEC. Due to its prevalence and complexity, the existing routing technologies in PEC need to be optimized. Specifically, key factors such as overall network traffic, user access latency, and resource utilization of edge nodes should be considered to adapt to the dynamic requirements of user request services and network topology. In order to address the challenges produced by these factors, an adaptive routing in P2P-based Edge Cloud is proposed, which is named ARPEC. In our approach, a target edge node selection scheme based on message activity and network topology is proposed, aiming to minimize the load on edge node and user access latency. Furthermore, to minimize system overhead, sensor data routing is mapped to minimum cost maximum flow (MCMF) graph. On this basis, a target edge node selection algorithm based on a grey linear regression combination prediction model is designed, and an incremental MCMF algorithm based on belief propagation (BP) is proposed. The evaluation results show that our approach can effectively improve PEC transmission performance and user experience.

Research on Services Requests and Utilisation Status and Satisfaction in a Family Health Center in Istanbul

Aim: In the family practice, the family doctor accompanied with the family health center employee renders integrated services to people registered in their department from physical, psychological and social points. The purpose of this research is to investigate the cognition, utilization, and satisfaction state of family practice services by the public of a family health center found at any district of Istanbul. Method: The research in question was done at the prefecture of Istanbul, at a family health center between the dates September 2013 and January 2014. We have applied the Cognition, Utilization and Satisfaction State of services survey (HBYMDA) developed by the researcher, we have collected data through the face-to-face method and applied the Patients Evaluate General/ Family Practice (EUROPEP) survey. The total points for HBYMDA was 24. Whereas the total point for EUROPEP was 115. Results: 302 (76%) of the persons who participated in the survey were women, and 98 (24%) men. The average age of the group was 37.17±14.10 years. The average age of women was 42±13.04 and 42.57±15.85 for men. The average of the total points for HBYMDA was calculated as 11.54±5.80. The average total point of EUROPEP was 93.87±21.32. The services that persons know the most, use the most and are satisfied the most are listed respectively, as following inspection, diagnosis and therapy, injection and medical dressing services, emergency services, free laboratory services. Whereas the services that participants know the less, use the less and are satisfied the less are listed respectively as following therapy of tuberculosis through direct observation, taking measures concerning contagious disease towards people and environment, military examination, and periodic examination. Conclusion: It is determined that the group of low social-economical-cultural uses more family practice services and that situation is related to the knowing, utilization and satisfaction state of the services.

Age of Information Optimization for Privacy-Preserving Mobile Crowdsensing

Mobile crowdsensing (MCS)-enabled data collection can be implemented in a cost-effective, scalable, and flexible manner. However, joint sensing data freshness and security assurance have not been fully investigated in the current research. To address these two concerns, the potential game and homomorphic encryption-based joint Age of Information (AoI) optimization and privacy-preservation scheme for MCS is put forward in this paper. At first, the AoI minimization and privacy preservation-oriented MCS system framework is established. Then, the AoI-based spectrum access strategies are derived by a potential game in detail, where the stochastic learning algorithm is used to reach the Nash Equilibrium (NE) solution. Next, based on the somewhat homomorphic encryption method, the encrypted sensing data can be submitted to the service provider (SP) for further processing, where the data content can only be known to mobile workers (MWs) and service requester (SR) with permission. Finally, the numerical results show that our proposed MCS system can simultaneously guarantee data freshness and system security at an acceptable cost.

Dynamic Optimization for Mobile Edge Computing: A Comparative Study

Edge computing EC is an advanced technology where a server at the edge resource is allocated at the network’s edge, closely to end users, mobile devices, sensors, and the developing Internet of Things (IoT). To date, a large number application using edge computing concept has developed and equipped in the market. However, “Edge” refer to continuum network resources as well as computing; through cloud center of data and the sources of data. The Edge is not limited to request service or contents; it is extended to perform tasks like computing from the cloud. One main reason to get well acceptance to the research community is; the edge is able to offload computing, store the data, processing and caching, request distribution and service delivery to the user from the cloud. In this paper, extensive experiments are conducted which leads to a comparison of several methods in order to solve two problems e.g. Power Allocation problem using Non-Cooperative Game model based on sub-Gradient NCGG and Nertia-Weighted Particle Swarm Optimization PSO methods. Similarly, Joint Request Offloading and Computing Resource Scheduling JRORS using MO-NSGA and Binary article Swarm Optimization BPSO will explored to check their effectiveness and efficiency to our day to day computing operation.

Діяльність психологічної служби в роботі благодійних та громадських організацій

The article examines the mechanism of responding to a request from a client and the organization of providing psychological services in the work of charitable and public organizations. The main tasks of a psychological service are diagnostics and normalization of mental state of the recipient of services; restoration of disturbed mental state to an optimal level of mental health; correction of the personality of the recipient of services to ensure his or her efficient functioning in society; formation and restoration of adaptive mechanisms; conducting psychoprophylaxis; formation of positive reactions, motivations, social attitudes towards life and cognitive activity; providing assistance in establishing or restoring constructive relations with the family and social environment. In particular, seven types of psychological services are distinguished: psychological diagnostics, crisis psychological counseling, psychological counseling, psychotherapy, psychological support and accompaniment, group work and psychological first aid. Based on the study of the work of several charitable and public organizations, the main requests for psychological services were identified to be family relations, sexual problems, contacts with peers, education and career guidance, gender-based violence, somatic and mental health, general and psychophysiological development. We achieved the set goal, which involved studying the system of psychological services in the structures of charitable and public organizations. In the article, we described the types and the procedures of providing psychological services and its documentation.

Cleaning Up the Neighborhood: White Influx and Differential Requests for Services

Visible signs of disorder serve as markers of difference across urban space. Sociological theory suggests that variation in collective social control efforts contributes to variation in physical disorder. However, how structural characteristics shape differences in informal social control remains underexplored because of limited data on disorder and social control. Using city service request data and a novel dataset drawing on Google Street View imagery and computer vision methods, the authors examine the neighborhood characteristics associated with propensities to request trash-related services across five large U.S. cities. The authors find that socioeconomically advantaged neighborhoods and those with fewer minority and foreign-born residents have higher propensities. However, an increase in White residents, but not necessarily an increase in high–socioeconomic status residents, is strongly associated with greater propensities. The authors argue that incoming White residents introduce unique dynamics of social control that are not necessarily collective, thereby affecting spatial inequality and power relations within their new neighborhoods.

Evaluating the Impact of Assignment Group and Category Classification Prediction of Incoming Service Requests on the Perceived Service Quality: A Quasiexperimental Study in the Enterprise Software Industry

Evaluating the Impact of Assignment Group and Category Classification Prediction of Incoming Service Requests on the Perceived Service Quality: A Quasiexperimental Study in the Enterprise Software Industry

Detection and Classification of Botnet Traffic using Deep Learning with Model Explanation

Distributed denial-of-service attacks are a kind of malicious attempts among many others that make target services unavailable to legitimate users by using a large number of bots, which send many service requests exceeding the processing capacity of the services. Detection of botnet traffic is therefore critical to maintaining the availability and quality of the services. In contrast, identifying the type of botnet attacks helps system administrators quickly determine which part of the computer and network system is under attack. Current works focus on rule-based detection, which sets rules in the network firewall to drop suspicious traffic that matches the rules. With the emergence of machine learning and deep learning (ML/DL), several preliminary works have been developed to learn botnet traffic behavior and perform detection. However, the performance of existing ML/DL models can be further improved and their decision/prediction are not transparent, making it hard for users to interpret and trust the results. In this work, we develop a novel deep learning model for botnet detection and classification combined with its ability of explaining the decision of the model. We first leverage latent representation of traffic features generated using convolutional neural networks to detect whether a traffic record is generated by a bot then determine the type of bots. We adopt an existing explainable framework to interpret the prediction of the developed deep learning model. We perform extensive experiments with real network traffic as well as synthetic traffic generated by IXIA BreakingPoint System. We compare the developed model with existing models on various performance metrics. The experimental results show that the developed model outperforms the existing machine learning models with an improvement of up to <inline-formula><tex-math notation="LaTeX">$15\%$</tex-math></inline-formula> for all performance metrics while providing a clear explanation of the model decision.