The explosion of online streaming services in the Internet-of-Things (IoT) ecosystem poses new difficulties in detecting anomalies in real-time and continuous data. The IoT data anomalies are classified into long-term and short-term, with unique characteristics that make it difficult to develop a common detection mechanism for both. The existing techniques require excessive training data and suffer from high variability. This paper overcomes these challenges by proposing a Variance profile Exploitation for Anomaly Detection (VEAD) scheme using discrete wavelet transform and k-means clustering. VEAD is initialized by a fast training phase with a single data segment, from which a sensor variance profile is created. This variance profile reflects the degree of deviation in the collected data from different sensors at a specific time period and is continuously updated by integrating new data segments for effective anomaly detection. Overlapping data collection in the detection phase shows correlations among consecutive data segments, leading to improved detection accuracy (ACC). The Intel Berkeley Research Lab dataset with injected synthetic anomalies is used to perform numerical experiments. A comparative performance evaluation with state-of-the-art methods confirms the effectiveness of VEAD in achieving a higher ACC and a lower false-positive rate (FPR). Notably, 95% and 97% ACCs are achieved in detecting long-term and short-term anomalies, respectively. The high specificity of VEAD is also revealed by the low FPR of at most 2% in all cases. The low computational complexity and fast anomaly detection make VEAD suitable for deployment in live systems with massive data streams.

The main purpose of this paper is to show the existence of a sequence of infinitely many small energy solutions to the nonlinear elliptic equations of Kirchhoff–Schrödinger type involving the fractional p-Laplacian by employing the dual fountain theorem as a key tool. Because of the presence of a non-local Kirchhoff coefficient, under conditions on the nonlinear term given in the present paper, we cannot obtain the same results concerning the existence of solutions in similar ways as in the previous related works. For this reason, we consider a class of Kirchhoff coefficients that are different from before to provide our multiplicity result. In addition, the behavior of nonlinear terms near zero is slightly different from previous studies.

Multi-access Edge Computing (MEC) paradigm allows devices to offload their intensive service tasks that require high Quality of Experience (QoE). Devices mobility forces services to migrate between MECs to maintain QoE in terms of delay. The decision on when to migrate a service requires a cost and QoE tradeoff, and destination MEC selection needs to be done upon latency and resource availability constraints to minimize migrations. To this end, we propose a novel Resource-Aware Service Migration (RASM) mechanism using Deep Q-Network (DQN) to make migration decisions by achieving tradeoff between the QoE in terms of delay and migration cost. Moreover, DQN learns the best policy for maximizing QoE by selecting the migration destination based on the MECs proximity to the device and estimated resource availability at the servers using queuing model. Results show faster convergence to optimal policy, reduced average end-to-end service delay by 27%, and smaller service rejection rate by 24% comparing to the state-of-the-art.

Importance of user mobility has rapidly increased in 5G due to reduced cell sizes, management of Multi-access Edge Computing (MEC), and ultra-low latency services. Reactive nature of existing management systems is a bottleneck, and it can be solved by building proactive systems that exploit temporal characteristics of time-series mobility data to predict long-term user movement (i.e.path). However, user mobile path prediction with useable accuracy is a challenging task, particularly for lengthy target trajectories. This paper adopts general approaches to propose two models for predicting mobile path with high accuracy. Step Forward Iteration (SFI) model is based on recursive approach, whereas Encoder-Decoder (ED) model follows multi-output approach, and both the models use Long-Short Term Memory (LSTM) as the learning unit. Training and testing of these models is done on mobility datasets from the wireless network of Pangyo ICT Research Center, Korea and one of the Korean mobile operators. The experiment results show viability of the proposed models for leveraging mobile network management, as they outperform state-of-the-art GRU with attention (GRU-ATTN) and Transformer Network (TN) models. The highest prediction accuracies achieved for 3, 5, and 7 steps of target sequences (i.e.predicted mobile path) in the campus dataset are 96%, 90%, and 87%, respectively.

Kāḇôḏ in kəḇôḏ YHWH has been translated predominantly with abstract words such as “glory” or “presence” throughout the Hebrew Bible. Some scholars argue that kəḇôḏ YHWH simultaneously marks the divine presence and hides the divine essence. These understandings do not capture the specific function and signification of kəḇôḏ YHWH in the pentateuchal Priestly source (P and H). In P and H, kəḇôḏ YHWH conveys the imagery of radiant, fiery clothing, comparable to Mesopotamian melammu. The deity always appears before the public with kəḇôḏ YHWH, whereas the text never says that the deity meets anyone privately with it. kəḇôḏ YHWH expresses the royal aspect of the deity who requires honor and submission. This understanding of kəḇôḏ YHWH in P reveals that the Priestly God is not abstract, enigmatic, or transcendent.

Paging is a critical operation in 5G mobile networks for localization of User Equipment (UE) in idle mode. Current multistep paging method increases the signaling exponentially and delay linearly, and analysis of a commercial Access and Mobility management Function (AMF) reports that paging accounts for 27.9% of AMF total traffic. To reduce signaling overhead and service delay by increasing the paging success rate, this paper presents a Deep Learning (DL) driven intelligent Paging as a Service (iPaaS) over a distributed NWDAF architecture based on serverless computing. iPaaS clusters UEs based on their movement patterns and feed cluster ID, movement history, and elapsed time in idle mode of a UE into DL model for UE localization prediction. Prediction results are used in two proposed schemes: iPaaS for Reduced Signaling (iPRS) and iPaaS for Reduced Delay (iPRD), which focus on cutting down signaling overhead and delay, respectively. Performance of iPaaS with two proposed schemes is evaluated on 110 million handover logs that are collected over 15 days from a commercial 5G network. Results show that iPRS reduces the signaling overhead up to 94% and iPRD reduces the delay up to 43% comparing to current method.

Purpose: Are board ties among competitors harmful to customers? The prevalent assumption on board ties among competitors is that they harm customer benefits. This study examines the mechanism by which board ties with competitors result in an outcome conducive to customers.Design/methodology/approach: Based on a sample of 79 savings banks in South Korea, we investigate the extent of banks’ board ties with other banks and their engagement in a project financing (PF) consortium from 2014 to 2020. The generalised least square was adopted to test the hypotheses. We also performed several supplemental analyses to further support our results.Findings/results: Savings banks with greater board ties with rivals provided more financial opportunities to their customers by forming PF consortia more actively with other banks. Furthermore, the positive impact of board ties on banks’ participation in a PF consortium increases, especially when the proportion of external shareholders is smaller or when savings banks are family firms.Practical implications: Outside directors can not only play the role of monitoring the management but also serve as assistants who can help banks provide financial services (or products) that banks could not provide individually.Originality/value: While prior studies have clearly recognised the negative impacts on customers of board-friendship ties among rivals, little attention has been paid to the potential mechanism by which board ties among competing firms can benefit customers. This study challenges the dominant assumption by demonstrating that savings banks with greater board ties with other banks provide more financial opportunities to their economically weak customers.Contribution: Finally, this study contributes to the family business literature by providing insight into how the unique characteristics of family firms in strategic choices make outside directors contribute as assistants than supervisors.

Software-Defined Networking (SDN) separates control from network elements and logically centralizes it in SDN controller to provide global view and control of the network. Network elements, such as switches, only forward data using entries in the flow tables that are installed by the controller. The capacity of flow tables is limited and requires continuous management. Several studies have proposed eviction strategies to make space for new entries in the flow tables, but they assume 1:1 mapping between entries and incoming flows. This assumption is a major limitation, as in real networks many incoming flows can be handled by a single Aggregate Flow Entry (AFE). This paper handles this limitation by proposing Deep Reinforcement Learning (DRL) framework for eviction of AFEs. The proposed framework calculates the degree of AFEs (i.e., how many flows it entertains) along with other parameters to select AFE for eviction, where main objective is to minimize flow table overflows. The experiment results show that the proposed framework reduces the number of overflows by 37%, flow reinstallation by 87%, and the control signaling overhead by 45 % compared to the Random and Least Recently Used Algorithm (LRU).

This paper articulates a way of the hearer's bodily experience of God in preaching by understanding it as a ritual knowledge that gained through the hearers' bodily practices in preaching and worship. In the application of ritual theory, embodied cognition theory, and social memory theory, this essay suggests ritual knowledge can be described as an embodied, imaginative, and social knowledge. On the basis of this concept of ritual knowledge, this paper will discuss how the hearer's experience in preaching can be understood in terms of ritual knowledge. As a result, it argues that the divine encounter in preaching is not merely a result of a cerebral intellectual understanding of the preaching contents but an event that may occur when the lived bodies of the hearers meet the Spirit in words and actions.