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ABSTRACT Context‐awareness is increasingly recognised as a fundamental principle in the development of ubiquitous computing and ambient intelligence. By leveraging contextual data about users and their environments, systems can gain a deeper understanding of the evolving user situation. This empowers them to dynamically adapt their operations, leading to optimised resource utilisation, enhanced decision‐making, and ultimately, greater user satisfaction. However, a critical challenge lies in effectively representing user situations with a high degree of expressiveness. While ontology‐based data models have emerged as a promising approach due to their ability to handle the inherent heterogeneity of context information, existing ontologies have limitations in terms of information coverage, data heterogeneity and uncertainties consideration, and reusability across various application domains. This paper addresses these limitations by proposing uCSN, an ontology that builds upon and extends the Data Privacy Vocabulary (DPV), Semantic Sensor Network (SSN) and W3C Uncertainty ontologies, to provide a rich and expressive vocabulary for representing diverse user situations. We evaluate uCSN based on its consistency, accuracy, clarity and performance. [ABSTRACT FROM AUTHOR]

Drama learning is helpful for English speaking, however, few studies provided students with opportunities to practice drama conversations individually. This study proposed a Context-Awareness Smart Learning Mechanism (CASLM) and integrated into SmartVpen that consisted of context-aware learning content, context-aware input assistance, oral recognition feedback, peer cooperative learning, and smart conversation robot. The participants were 68 eighth grade-students divided into three groups: an experimental group (EG) who used SmartVpen, a control group 1 (CG1) who used typical camera and voice recorder, and a control group 2 (CG2) who used papers and pencils. The results showed the EG outperformed the other groups concerning oral and conversational skills, which indicated the use of SmartVpen had significant effects in both English oral speaking and conversational skills. Additionally, the number of time to complete conversation practices can predict students' oral performance by 30%. Furthermore, the results also showed the EG tend to practice drama conversations more frequently than the CG1, which demonstrated practicing English drama conversations using SmartVpen can effectively improve students' learning motivation. Thus, we suggested English conversations practice activities should be conducted in authentic context with SmartVpen to support students' speaking and facilitate them to apply what they learned in real-life situations. [ABSTRACT FROM AUTHOR]

Recent advancements in wearable smart devices, medical internet of things, cloud computing, wireless communications, and AI-based technologies have enabled personalized remote health care for patients with chronic diseases. Covid pandemic period has exposed the shortfall of the healthcare system, where there was a massive shortage of doctors, nurses, medical supplies, hospital beds, and other healthcare infrastructure, which has affected many patients with chronic diseases who needed constant monitoring and consultation with doctors. This has affirmed the necessity of remotely monitoring the patients to predict their requirements for medicines, treatment, etc., and to avoid any unusual severe condition. In this work, we presented the Patient Model to monitor the patient's activities and remotely identify and fulfil their treatment requirements. The framework monitors the patients and depending on the diagnosis provides personalized remote health care services such as telemedicine, medical tests, diet plans, etc., along with an ambulance facility if needed. The proposed framework uses a CNN and other machine learning algorithm to predict the required personalized healthcare service requirements. The simulation results show that the proposed framework, using the patient’s model, and CNN algorithm significantly improves the precision and recall of the prediction and reduces the time to predict the requirements.

Salient object detection (SOD) focuses on distinguishing the most conspicuous objects in the scene. However, most related works are based on RGB images, which lose massive useful information. Accordingly, with the maturity of thermal technology, RGB-T (RGB-Thermal) multi-modality tasks attain more and more attention. Thermal infrared images carry important information which can be used to improve the accuracy of SOD prediction. To accomplish it, the methods to integrate multi-modal information and suppress noises are critical. In this paper, we propose a novel network called Interactive Context-Aware Network (ICANet). It contains three modules that can effectively perform the cross-modal and cross-scale fusions. We design a Hybrid Feature Fusion (HFF) module to integrate the features of two modalities, which utilizes two types of feature extraction. The Multi-Scale Attention Reinforcement (MSAR) and Upper Fusion (UF) blocks are responsible for the cross-scale fusion that converges different levels of features and generate the prediction maps. We also raise a novel Context-Aware Multi-Supervised Network (CAMSNet) to calculate the content loss between the prediction and the ground truth (GT). Experiments prove that our network performs favorably against the state-of-the-art RGB-T SOD methods. [ABSTRACT FROM AUTHOR]

Nowadays, ubiquitous technology makes life easier, especially devices that use the internet (IoT). IoT devices have been used to generate data in various domains, including healthcare, industry, and education. However, there are often problems with this generated data such as missing values, duplication, and data errors, which can significantly affect data analysis results and lead to inaccurate decision making. Enhancing the quality of real-time data streams has become a challenging task as it is crucial for better decisions. In this paper, we propose a framework to improve the quality of a real-time data stream by considering different aspects, including context-awareness. The proposed framework tackles several issues in the data stream, including duplicated data, missing values, and outliers to improve data quality. The proposed framework also provides recommendations on appropriate data cleaning techniques to the user to help improve data quality in real time. Also, the data quality assessment is included in the proposed framework to provide insight to the user about the data stream quality for better decisions. We present a prototype to examine the concept of the proposed framework. We use a dataset that is collected in healthcare and process these data using a case study. The effectiveness of the proposed framework is verified by the ability to detect and repair stream data quality issues in selected context and to provide a recommended context and data cleaning techniques to the expert for better decision making in providing healthcare advice to the patient. We evaluate our proposed framework by comparing the proposed framework against previous works. [ABSTRACT FROM AUTHOR]

The age of pervasive computing has initiated a boom in the development of adaptive context-aware learning environments (ACALEs), i.e., systems that are capable of detecting a learner's context and providing adaptive learning services based on this context. Many of the existing educational systems were developed as standalone applications for specific or a small range of adaptive educational scenarios. It would be extremely helpful for developers and educators to have a unified framework that provides an infrastructure for the development of ACALEs. In this study, we propose Lightlore—an adaptation framework that enables the development of different types of ACELEs for a wide range of learning scenarios in formal and informal settings. We first used scenario-based design (SBD) as the design methodology for creating a conceptual model of Lightlore. Educational scenarios were adopted from the results of a previous literature review. We then developed a proof-of-concept implementation of Lightlore, with a hypermedia system for learning data structures that uses the adaptation service of Lightlore. This implementation is essentially an adaptation infrastructure and a programming API for creating new (or transforming existing) adaptive and context-aware educational services. It exploits the experience API (xAPI), a modern e-learning standard and learning record store, thus making coupling with existing learning environments easier. We expect that diverse types of users will benefit from using Lightlore, such as learners, educators, learning environment developers, and researchers on educational technologies. [ABSTRACT FROM AUTHOR]

Recent advancements in wearable smart devices, medical internet of things, cloud computing, wireless communications, and AI-based technologies have enabled personalized remote health care for patients with chronic diseases. Covid pandemic period has exposed the shortfall of the healthcare system, where there was a massive shortage of doctors, nurses, medical supplies, hospital beds, and other healthcare infrastructure, which has affected many patients with chronic diseases who needed constant monitoring and consultation with doctors. This has affirmed the necessity of remotely monitoring the patients to predict their requirements for medicines, treatment, etc., and to avoid any unusual severe condition. In this work, we presented the Patient Model to monitor the patient's activities and remotely identify and fulfil their treatment requirements. The framework monitors the patients and depending on the diagnosis provides personalized remote health care services such as telemedicine, medical tests, diet plans, etc., along with an ambulance facility if needed. The proposed framework uses a CNN and other machine learning algorithm to predict the required personalized healthcare service requirements. The simulation results show that the proposed framework, using the patient's model, and CNN algorithm significantly improves the precision and recall of the prediction and reduces the time to predict the requirements. [ABSTRACT FROM AUTHOR]

Exploratory data analysis (EDA) has emerged as a critical tool for users to gain deep insights into data and unearth hidden patterns. The integration of recommendation algorithms has enhanced its capabilities and further popularized its utilization. Most recommendation-based EDA methods concentrate on the extraction of pivotal insights from datasets, and the taxonomy of these insights is well-established. However, the support for further analytical endeavors to expand these initial findings remains constrained, as evidenced by the restricted scope of analytical intents that are tailored to specific scenarios. Moreover, these systems often lack sufficient context-awareness capabilities, failing to equip users with the necessary tools for a thorough exploration of extensive recommendations. To address these limitations, we introduce Qutaber, a task-based EDA system with enriched context-awareness. We first summarize six core analytical tasks tailored for EDA scenarios through literature reviews and expert interviews. Then, Qutaber integrates the use of small multiples, enhanced with a multi-metric re-ranking function, to enable a thorough and efficient examination of expanded charts pertaining to various analytical tasks. Furthermore, a machine learning method is leveraged to characterize the semantic features of these charts for a holistic landscape of recommended charts. Finally, a case study using a real-world dataset demonstrates Qutaber's practical application, followed by a user study to further evaluate the usability of the proposed techniques. Our findings illustrate that Qutaber facilitates an effective and context-rich EDA experience for users. [ABSTRACT FROM AUTHOR]

This work presents the hypothesis that guided the research efforts and a summary of the contributions of the doctoral thesis 'Learning and adaptation of strategies in automated negotiations between context-aware agents'. Succinctly, the thesis focuses on agents for automated bilateral negotiations that make use of the context as a key source of information to learn and adapt negotiation strategies in two levels of temporal abstraction. At the highest level, agents employ reinforcement learning to select strategies according to contextual circumstances. At the lowest level, agents use Gaussian Processes and artificial Theory of Mind to model their opponents and adapt their strategies. Agents are then tested in two Peer-to-Peer markets comprising an Eco-Industrial Park and a Smart Grid. The results highlight the significance for the automation of bilateral negotiations of incorporating the context as an informative source. [ABSTRACT FROM AUTHOR]

Integrating context awareness into the Internet of Things systems is essential for enhancing their adaptability to their context, particularly their user preferences and behaviors. This paper proposes an approach to model and develop context-aware self-adaptive IoT systems, capable of adapting their actions according to their users' preferences. The approach consists of three main axes. The first axis involves establishing an overview of the system architecture that provides a high-level understanding of the various components of a context-aware IoT system. The second axis concerns the creation of a context-aware IoT systems meta-model, encapsulating the essential elements, relationships, and dependencies governing context awareness within the IoT system in a domain-independent manner. The third axis proposes a reinforcement learning reasoning process to enable intelligent decision-making within context-aware IoT systems. To validate the feasibility of the proposed approach, a simulation was conducted using the OpenAI Gym framework to emulate a context-aware smart home system. The results highlight the feasibility of the approach, and its potential to enhance real-life IoT systems' awareness of their users' context. [ABSTRACT FROM AUTHOR]

Modern service-based processes in mobile environments are highly complex due to the necessary spatial–temporal coordination between multiple participating users and the consideration of context information. Due to the dynamic nature of mobile environments, disruptive events occur at runtime, which require a re-selection of the planned service compositions respecting multiple users and context-awareness. Thereby, when re-selecting services the features performance, solution quality, solution robustness and alternative solutions are essential and contribute to the efficacy of service systems. This paper presents an optimization-based heuristic technique based on a stateful representation that uses a region-based approach to re-select services considering multiple users, context information and in particular disruptive events at runtime. The evaluation results, which are based on a real-world scenario from the tourism domain, show that the proposed heuristic is superior compared to competing artifacts. [ABSTRACT FROM AUTHOR]

Abstract The research addresses energy consumption, latency, and network reliability challenges in wireless sensor network communication, especially in military security applications. A multi-agent context-aware model employing the belief-desire-intention (BDI) reasoning mechanism is proposed. This model utilizes a semantic knowledge-based intelligent reasoning network to monitor suspicious activities within a prohibited zone, generating alerts. Additionally, a BDI intelligent multi-level data transmission routing algorithm is proposed to optimize energy consumption constraints and enhance energy-awareness among nodes. The energy optimization analysis involves the Energy Percent Dataset, showcasing the efficiency of four wireless sensor network techniques (E-FEERP, GTEB, HHO-UCRA, EEIMWSN) in maintaining high energy levels. E-FEERP consistently exhibits superior energy efficiency (93 to 98%), emphasizing its effectiveness. The Energy Consumption Dataset provides insights into the joule measurements of energy consumption for each technique, highlighting their diverse energy efficiency characteristics. Latency measurements are presented for four techniques within a fixed transmission range of 5000 m. E-FEERP demonstrates latency ranging from 3.0 to 4.0 s, while multi-hop latency values range from 2.7 to 2.9 s. These values provide valuable insights into the performance characteristics of each technique under specified conditions. The Packet Delivery Ratio (PDR) dataset reveals the consistent performance of the techniques in maintaining successful packet delivery within the specified transmission range. E-FEERP achieves PDR values between 89.5 and 92.3%, demonstrating its reliability. The Packet Received Data further illustrates the efficiency of each technique in receiving transmitted packets. Moreover the network lifetime results show E-FEERP consistently improving from 2550 s to round 925. GTEB and HHO-UCRA exhibit fluctuations around 3100 and 3600 s, indicating variable performance. In contrast, EEIMWSN consistently improves from round 1250 to 4500 s.

Nowadays, in a turbulent environment, enterprise must be proactive and even predictive to survive in front of the plenty natures of dynamics. Achieving the desired proactivity and predictability, supposes that the enterprise works in advance on its internal transformation plans, but also develops an awareness to its context, to intercept, analyze and simulate any potential trigger of change that could affect its stability or more precisely its state of equilibrium. In this work, the concept of enterprise architecture is considered as a tool for establishing alignment between business, strategy, and IT. This paper is a continuation to previous works seeking for a dynamic model of enterprise architecture, able to respond in an autonomic manner to the turbulent environment. The self-adaptiveness was highlighted basically using the well-known loop, monitor, analyze, plan, execute and Knowledge. The use of Case-Based Reasoning was suggested to store a collection of problems and their resolutions, then using algorithms of similarity and adaptation to suggest the problem's best fit solution. To make the reasoning mechanism and similarity process as reliable as possible, it was important to maximize experiences and expand the knowledge base. For thus, the proposition was to share knowledge bases. In this paper, the context awareness is considered as second source to enrich the knowledge base and to stay vigilant and sensitive to triggers of change. [ABSTRACT FROM AUTHOR]

The research addresses energy consumption, latency, and network reliability challenges in wireless sensor network communication, especially in military security applications. A multi-agent context-aware model employing the belief-desire-intention (BDI) reasoning mechanism is proposed. This model utilizes a semantic knowledge-based intelligent reasoning network to monitor suspicious activities within a prohibited zone, generating alerts. Additionally, a BDI intelligent multi-level data transmission routing algorithm is proposed to optimize energy consumption constraints and enhance energy-awareness among nodes. The energy optimization analysis involves the Energy Percent Dataset, showcasing the efficiency of four wireless sensor network techniques (E-FEERP, GTEB, HHO-UCRA, EEIMWSN) in maintaining high energy levels. E-FEERP consistently exhibits superior energy efficiency (93 to 98%), emphasizing its effectiveness. The Energy Consumption Dataset provides insights into the joule measurements of energy consumption for each technique, highlighting their diverse energy efficiency characteristics. Latency measurements are presented for four techniques within a fixed transmission range of 5000 m. E-FEERP demonstrates latency ranging from 3.0 to 4.0 s, while multi-hop latency values range from 2.7 to 2.9 s. These values provide valuable insights into the performance characteristics of each technique under specified conditions. The Packet Delivery Ratio (PDR) dataset reveals the consistent performance of the techniques in maintaining successful packet delivery within the specified transmission range. E-FEERP achieves PDR values between 89.5 and 92.3%, demonstrating its reliability. The Packet Received Data further illustrates the efficiency of each technique in receiving transmitted packets. Moreover the network lifetime results show E-FEERP consistently improving from 2550 s to round 925. GTEB and HHO-UCRA exhibit fluctuations around 3100 and 3600 s, indicating variable performance. In contrast, EEIMWSN consistently improves from round 1250 to 4500 s. [ABSTRACT FROM AUTHOR]

Nowadays, enterprises are required to take the uncertainty of the environment as decisive factor of success. For this reason, Enterprises should be prepared up-stream to react dynamically to the turbulent context. Considering that enterprise architecture is a tool drawing a blueprint that gives a holistic view of the enterprise, this blueprint should be able to represent this awareness to context and implements the techniques and mechanisms to react in a dynamic manner depending on the triggers of change. In this paper, the proposed model stipulates a "context-awareness" that monitors the internal and external context, and then adapt its reaction in alignment with the prefixed goals. The operationalization of our conception is realized through the monitor-analyze-plan-execute-knowledge (MAPE-K) loop, the case-based reasoning and machine learning techniques organized and orchestrated through a global algorithm of 6 main functions to monitor, compare, analyze, plan, execute and enrich the knowledge base. The results are verified in the light of a case study that demonstrates the applicability of our proposed model. [ABSTRACT FROM AUTHOR]

The ever-increasing demand for the wireless communications specially in sub-6 GHz frequency ranges has led to radio resource scarcity where opportunistic spectrum access is its main solution. An online spectrum decision and prediction system can assist cognitive radio users in seeking idle frequency bands for opportunistic use. However, previous studies have not considered the use of crowd-sensing technique to collect spectrum and contextual information to present a hybrid spectrum decision/prediction service. In this paper, we propose a novel cloud-based service for spectrum availability decision and prediction, which brings more contextual parameters into the decision with the aim of improving the quality of decision. Location, time, and velocity of sensing nodes, the density of buildings around sensing nodes, and weather status have been considered as context information. In the proposed method, spectrum availability data and some of the mentioned context parameters are collected through crowd-sensing. Artificial neural network (ANN) classifiers are suggested to decide about the status of spectrum bands in the proposed architecture. We also propose a spectrum prediction service in our architecture to predict the future of spectrum bands and recommend ANN and k-nearest neighbor algorithms for prediction. The proposed architecture has been implemented and evaluated. Experimental results show that using the addressed contextual information, the quality of spectrum availability decision is improved. [ABSTRACT FROM AUTHOR]

Today, ubiquitous computing is gaining traction as a new generation capable of addressing the vast and scalable changes in the quality and amount of data processed and used by the public. As a result, various publications have tackled the problem of service composition by adopting methodologies that take into account the ideas of intention and context, but without actually merging them as concordant and relevant aspects in the service composition process. In this context, we propose an approach for service composition, guided by the user's intention and context, which is inspired by different works that have addressed the topic of service composition by exploiting artificial intelligence (AI) planning and the concepts related to intention and context. The main idea behind this approach is the implementation of conceptual and architectural aspects that allow the composition of services, independently of any platform, programming language, or specific tool, while ensuring the integrity of the handled data and the quality of the offered services. In this sense, we present in this paper a method to conceive a service composition problem into an AI planning problem, which is parameterized by the user's contextual data and seeks to achieve a goal related to a fixed intention by implementing an AI planner that exploits and manipulates the functionalities offered by a genetic algorithm (GA), which has as a goal to propose solutions that solve the conceived planning problem (composite services). [ABSTRACT FROM AUTHOR]

In today’s society, the harm of driving anger to traffic safety is increasingly prominent. With the development of human-computer interaction and intelligent transportation systems, the application of biometric technology in driver emotion recognition has attracted widespread attention. This study proposes a context-aware multi-modal driver anger emotion recognition method (CA-MDER) to address the main issues encountered in multi-modal emotion recognition tasks. These include individual differences among drivers, variability in emotional expression across different driving scenarios, and the inability to capture driving behavior information that represents vehicle-to-vehicle interaction. The method employs Attention Mechanism-Depthwise Separable Convolutional Neural Networks (AM-DSCNN), an improved Support Vector Machines (SVM), and Random Forest (RF) models to perform multi-modal anger emotion recognition using facial, vocal, and driving state information. It also uses Context-Aware Reinforcement Learning (CA-RL) based adaptive weight distribution for multi-modal decision-level fusion. The results show that the proposed method performs well in emotion classification metrics, with an accuracy and F1 score of 91.68% and 90.37%, respectively, demonstrating robust multi-modal emotion recognition performance and powerful emotion recognition capabilities.

The widespread adoption of electric vehicles (EVs) has introduced new challenges for stakeholders ranging from grid operators to EV owners. A critical challenge is to develop an effective and economical strategy for managing EV charging while considering the diverse objectives of all involved parties. In this study, we propose a context-aware EV smart charging system that leverages deep reinforcement learning (DRL) to accommodate the unique requirements and goals of participants. Our DRL-based approach dynamically adapts to changing contextual factors such as time of day, location, and weather to optimize charging decisions in real time. By striking a balance between charging cost, grid load reduction, fleet operator preferences, and charging station energy efficiency, the system offers EV owners a seamless and cost-efficient charging experience. Through simulations, we evaluate the efficiency of our proposed Deep Q-Network (DQN) system by comparing it with other distinct DRL methods: Proximal Policy Optimization (PPO), synchronous Advantage Actor-Critic (A3C), and Deep Deterministic Policy Gradient (DDPG). Notably, our proposed methodology, DQN, demonstrated superior computational performance compared to the others. Our results reveal that the proposed system achieves a remarkable, approximately 18% enhancement in energy efficiency compared to traditional methods. Moreover, it demonstrates about a 12% increase in cost-effectiveness for EV owners, effectively reducing grid strain by 20% and curbing CO2 emissions by 10% due to the utilization of natural energy sources. The system’s success lies in its ability to facilitate sequential decision-making, decipher intricate data patterns, and adapt to dynamic contexts. Consequently, the proposed system not only meets the efficiency and optimization requirements of fleet operators and charging station maintainers but also exemplifies a promising stride toward sustainable and balanced EV charging management.

Manufacturing processes of the future will rely on standards for asset interoperability and service orchestration. The Asset Administration Shell (AAS) facilitates information exchange among Industry 4.0 assets, while standardized Business Processes enable workflow execution in manufacturing systems. Combining these technologies provides agility and scalability to manufacturing systems by incorporating asset services within business processes. Service orchestration involves coordinating multiple services, which must be dynamic during runtime to manage unforeseen situations that may arise during the manufacturing process. Context information plays a crucial role in identifying such scenarios and selecting the most suitable devices/services in response, and the Semantic Web accurately represents this information. This paper proposes a context-aware approach for service orchestration using industrial asset services. Our contributions include (1) a component for Context-Aware Service Re-Selection. (2) a domain-specific ontology (DeviceServiceOnt) for Semantic Web-based context representation. And, (3) validation of our proposal in a manufacturing setting where robots are responsible for dispatching and distributing materials within a warehouse. Opportunities for future work are also highlighted, with a primary focus on enhancing workflow dynamicity with context-aware capabilities. [ABSTRACT FROM AUTHOR]

Abstract Recent technological advancements have led to a significant increase in digital documents. A document’s key information is generally represented by the keyphrases that provide the abstract description contained therein. With traditional keyphrase techniques, however, it is difficult to identify relevant information based on context. Several studies in the literature have explored graph-based unsupervised keyphrase extraction techniques for automatic keyphrase extraction. However, there is only limited existing work that embeds contextual information for keyphrase extraction. To understand keyphrases, it is essential to grasp both the concept and the context of the document. Hence, a hybrid unsupervised keyphrase extraction technique is presented in this paper called ContextualRank, which embeds contextual information such as sentences and paragraphs that are relevant to keyphrases in the keyphrase extraction process. We propose a hierarchical topic modeling approach for topic discovery based on aggregating the extracted keyphrases from ContextualRank. Based on the evaluation on two short-text datasets and one long-text dataset, ContextualRank obtains remarkable improvements in performance over other baselines in the short-text datasets.

Nowadays, ubiquitous technology makes life easier, especially devices that use the internet (IoT). IoT devices have been used to generate data in various domains, including healthcare, industry, and education. However, there are often problems with this generated data such as missing values, duplication, and data errors, which can significantly affect data analysis results and lead to inaccurate decision making. Enhancing the quality of real-time data streams has become a challenging task as it is crucial for better decisions. In this paper, we propose a framework to improve the quality of a real-time data stream by considering different aspects, including context-awareness. The proposed framework tackles several issues in the data stream, including duplicated data, missing values, and outliers to improve data quality. The proposed framework also provides recommendations on appropriate data cleaning techniques to the user to help improve data quality in real time. Also, the data quality assessment is included in the proposed framework to provide insight to the user about the data stream quality for better decisions. We present a prototype to examine the concept of the proposed framework. We use a dataset that is collected in healthcare and process these data using a case study. The effectiveness of the proposed framework is verified by the ability to detect and repair stream data quality issues in selected context and to provide a recommended context and data cleaning techniques to the expert for better decision making in providing healthcare advice to the patient. We evaluate our proposed framework by comparing the proposed framework against previous works.

Based on context-aware theory, this paper proposes an online anomaly detection and warning method for multi-angle transmission grid construction business processes. The business process model for transmission grid construction is constructed using a Petri net, and the data is preprocessed. The context in the business event log is initially refined, and the data in the model training is labeled using the supervised learning method. The sliding window Euclidean distance model is applied to build an anomaly detection model for the transmission grid construction business process, and the data is filtered by mean value aggregation to obtain the threshold value for anomaly monitoring and to realize anomaly detection and warning. This paper’s anomaly detection and early warning model is applied to carry out transmission grid construction business flow optimization practice in the G Power Supply Bureau of Yunnan Province, China. Since the optimization in June, the timeliness and accuracy of material matching of G Power Supply Bureau have been stable at more than 90%, and the execution rate of the operation plan and on-time completion rate of billing have also been maintained at a high level of about 95%. The number of cancellations and adjustments to transmission network construction operations has decreased from 20% to 30% to less than 7%, and the overall satisfaction score of transmission network construction business processes has reached 4.15.

Fog computing emerges as the new computing environment that stays in the proximity of end‐users and harnesses resources at the edge of the network to extend cloud‐facilities. It provides attractive solutions to the diverse range of Internet of Things (IoT) applications by executing them in the vicinity of end‐users. It is challenging to schedule these latency‐sensitive, computation‐intensive, and resource‐hungry applications on distributed, heterogeneous, and resource‐constrained Fog computing environment while ensuring time‐bound service delivery and satisfying Quality of Service (QoS) requirements of end‐users. In this paper, a context‐aware application scheduling technique is proposed for Fog computing environments that employs various parameters of device‐ and application‐level context to minimize service delivery time and satisfy QoS requirements of various IoT applications such as surveillance and game‐based applications. The performance of the proposed technique is evaluated in a simulated Fog environment and compared with baseline application scheduling techniques. The simulation results demonstrate that the proposed context‐aware scheduling techniques result in significant improvement in service delivery time and QoS compared to baseline scheduling techniques. [ABSTRACT FROM AUTHOR]

Recent technological advancements have led to a significant increase in digital documents. A document's key information is generally represented by the keyphrases that provide the abstract description contained therein. With traditional keyphrase techniques, however, it is difficult to identify relevant information based on context. Several studies in the literature have explored graph-based unsupervised keyphrase extraction techniques for automatic keyphrase extraction. However, there is only limited existing work that embeds contextual information for keyphrase extraction. To understand keyphrases, it is essential to grasp both the concept and the context of the document. Hence, a hybrid unsupervised keyphrase extraction technique is presented in this paper called ContextualRank, which embeds contextual information such as sentences and paragraphs that are relevant to keyphrases in the keyphrase extraction process. We propose a hierarchical topic modeling approach for topic discovery based on aggregating the extracted keyphrases from ContextualRank. Based on the evaluation on two short-text datasets and one long-text dataset, ContextualRank obtains remarkable improvements in performance over other baselines in the short-text datasets. [ABSTRACT FROM AUTHOR]

The paradigm of Smart product-service systems (Smart PSS) has emerged recently owing to the edge-cutting Information and Communication Technology (ICT) and artificial intelligence (AI) techniques. The unique features of Smart PSS including smartness and connectedness, value co-creation and data-driven design manner, enable the collection and analysis of large volume and heterogeneous contextual data to extract useful knowledge. Therefore, requirement elicitation, as a critical process for new solution (i.e. product-service) design, can be conducted in a rather context-aware manner, assured by those massive user-generated data and product-sensed data during the usage stage. Nevertheless, despite a few works on semantic modelling, scarcely any reports on such mechanism in today's smart, connected environment. Aiming to fill this gap, for the first time, a graph-based context-aware requirement elicitation approach considering contextual information within the Smart PSS is proposed. It leverages the pre-defined product, service, and condition ontologies together with Deepwalk technique, to formulate those concepts as nodes and their relationships as the edge of the proposed requirement graph. Implicit stakeholder requirements within a specific context can be further derived based on such interrelationships in a data-driven manner. To demonstrate its feasibility and effectiveness, an example of smart bike share system is addressed to illustrate the requirement elicitation process. It is hoped that this explorative study can offer valuable insights for the service providers who would like to extract requirements not only from the voice of customers but also from the user-generated data and product-sensed data. [ABSTRACT FROM AUTHOR]