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Cloud computing has revolutionized the way a variety of ubiquitous computing resources are provided to users with ease and on a pay-per-usage basis. Task scheduling problem is an important challenge, which involves assigning resources to users' Bag-of-Tasks applications in a way that maximizes either system provider or user performance or both. With the increase in system size and the number of applications, the Bag-of-Tasks scheduling (BoTS) problem becomes more complex due to the expansion of search space. Such a problem falls in the category of NP-hard optimization challenges, which are often effectively tackled by metaheuristics. However, standalone metaheuristics generally suffer from certain deficiencies which affect their searching efficiency resulting in deteriorated final performance. This paper aims to introduce an optimal hybrid metaheuristic algorithm by leveraging the strengths of both the Artificial Gorilla Troops Optimizer (GTO) and the Honey Badger Algorithm (HBA) to find an approximate scheduling solution for the BoTS problem. While the original GTO has demonstrated effectiveness since its inception, it possesses limitations, particularly in addressing composite and high-dimensional problems. To address these limitations, this paper proposes a novel approach by introducing a new updating equation inspired by the HBA, specifically designed to enhance the exploitation phase of the algorithm. Through this integration, the goal is to overcome the drawbacks of the GTO and improve its performance in solving complex optimization problems. The initial performance of the GTOHBA algorithm tested on standard CEC2017 and CEC2022 benchmarks shows significant performance improvement over the baseline metaheuristics. Later on, we applied the proposed GTOHBA on the BoTS problem using standard parallel workloads (CEA-Curie and HPC2N) to optimize makespan and energy objectives. The obtained outcomes of the proposed GTOHBA are compared to the scheduling techniques based on well-known metaheuristics under the same experimental conditions using standard statistical measures and box plots. In the case of CEA-Curie workloads, the GTOHBA produced makespan and energy consumption reduction in the range of 8.12–22.76% and 6.2–18.00%, respectively over the compared metaheuristics. Whereas for the HPC2N workloads, GTOHBA achieved 8.46–30.97% makespan reduction and 8.51–33.41% energy consumption reduction against the tested metaheuristics. In conclusion, the proposed hybrid metaheuristic algorithm provides a promising solution to the BoTS problem, that can enhance the performance and efficiency of cloud computing systems. [ABSTRACT FROM AUTHOR]

IoT enables the emergence and implementation of smart devices to address real-world problems and challenges. Today we are surrounded by various smart devices, such as smart phones, smart homes, smart cars, smart televisions, and smartwatches that assist us in making our lives easier and smoother. IoT is a conglomeration of multiple technologies at various layers to impart the best of ubiquitous and pervasive computing to deliver several benefits in a variety of application sectors such as medicine, agriculture, and industry. In an IoT setting, blockchain technology is used for addressing security challenges and eradicating third-party participation. Utilizing public networks for storing or transmitting health care images poses risks of eavesdropping, data breaches, and unauthorized access. Before uploading medical data to the decentralized network, encryption is required to prevent unauthorized access. The aim of this study is to integrate technologies to ensure transactions are conducted safely and securely. We proposed an IoT-Blockchain system based on chaos encryption scheme using Tinkerbell mapping to ensure medical data integrity and authenticity. The suggested approach is examined to assess performance parameters such as, key space analysis, key sensitivity analysis, Information Entropy (IE), histogram, correlation of adjacent pixels, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSIM). These findings demonstrated that the suggested method is extremely efficient in avoiding security breaches and guaranteeing information integrity. [ABSTRACT FROM AUTHOR]

Human activity recognition(HAR) is an important research focus in ubiquitous computing. It has been widely applied in various domains, such as smart homes, healthcare assistance, and sports training. Accurate human activity recognition directly impacts the performance of downstream tasks. Existing methods for human activity recognition primarily rely on extracting temporal or spatial features from the data. These features are used for the task of human activity recognition. The existing methods mainly use CNN or RNN models to model the Euclidean correlations among spatially adjacent sensors or channels. However, non-Euclidean pairwise correlations among all sensors or channels are even critical for accurate classification, which has been ignored by the existing methods. In this paper, we incorporate fine-grained spatial structural data into the model to overcome these limitations. A novel deep learning model for human activity recognition is proposed, which is called the fine-grained data-oriented Spatial-Temporal Graph Transformer network (STGT). The introduction of the STGT model can eliminate the limitations of existing spatial feature extraction methods by leveraging a novel data organization approach proposed in this study. This model enhances the spatial features within the time feature extraction module for effective spatio-temporal feature extraction. We conducted experiments on four large-scale real-world HAR datasets to evaluate its performance. The experimental results demonstrate the superiority of our method over state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

We are in the early stages of a new era of demographic research that offers exciting opportunities to quantify demographic phenomena at a scale and resolution once unimaginable. These scientific possibilities are opened up by new sources of data, such as the digital traces that arise from ubiquitous social computing, massive longitudinal datasets produced by the digitization of historical records, and information about previously inaccessible populations reached through innovations in classic modes of data collection. In this commentary, we describe five promising new sources of demographic data and their potential appeal. We identify cross‐cutting challenges shared by these new data sources and argue that realizing their full potential will demand both innovative methodological developments and continued investment in high‐quality, traditional surveys and censuses. Despite these considerable challenges, the future is bright: these new sources of data will lead demographers to develop new theories and revisit and sharpen old ones. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) has grown more important in the domains of pervasive computing, human behavior analysis, assistive health care, and Human–Computer Interaction (HCI). While Deep Learning (DL)-based methods have demonstrated remarkable efficiency in the identification of activities, the management of time series data remains a challenge. Nevertheless, a large number of models currently in use direct the effective extraction of temporal and geographical information from data on human behavior. In this paper, a fusion of a 1-dimensional convolutional neural network (1-DCNN) and Stacked long short-term memory (SLSTM) is proposed for HAR. This model automatically extracts geographical information from the raw body sensor data. 1-D CNN model can extract local features and the SLSTM model can extract long-term relationships in sequence data. In addition, a batch normalization layer was added after the convolution layer to reduce internal covariate shift, accelerate training, and reduce the need for dropout. Moreover, a Max Pooling Layer was applied next to the pooling layer to reduce model parameters. The final step involves applying the learned features to the Softmax layer to fully recognize human activities. The model performance is evaluated on two standard datasets MHEALTH and WISDM and achieves classification accuracies of 99.67% and 99.55% correspondingly. The results demonstrate that the recommended model outperformed the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The prevalence of mobile and ubiquitous computing equipment is transforming how learners conduct their education. Nevertheless, the bulk of educational content generated for desktop platforms is unacceptable for handheld gadgets. Furthermore, some materials that are irrelevant to the preferences of the learner or the contextual environment might reduce the effect of learning and elevate the cost of communication. Furthermore, there is a deficit in the academic structure for mobile learning. This paper proposes personalized adaptation techniques for customized modification contents. This in turn strives to offer adaptive content contingent on learner experience and device capabilities. On top of that, it recommends a few alternatives to producing intelligent and adaptive material for children. Numerous facets of the study are examined to employ the benefits of learner, device, and location modeling in tandem with mobile technology to provide a personalized spread of multimedia-rich resources at any time or anywhere. As an outcome, it delivers proper learning resources and applies the learning style most suitable to the demands and tastes of each learner (personalized learning). Our system enables several types of learning styles to pick from, but the most significant aspect is to pair each learner's characteristics with the right program and learning style. A novel chance for a broad range of learners to boost their knowledge in a tailored manner is made accessible by the adaptation of the material and styles, which enables the learning process to be dynamic. Additionally, our system's resilience and interaction have risen owing to multi-agent system modeling. [ABSTRACT FROM AUTHOR]

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis. [ABSTRACT FROM AUTHOR]

Mobile networks possess significant information and thus are considered a gold mine for the researcher's community. The call detail records (CDR) of a mobile network are used to identify the network's efficacy and the mobile user's behavior. It is evident from the recent literature that cyber-physical systems (CPS) were used in the analytics and modeling of telecom data. In addition, CPS is used to provide valuable services in smart cities. In general, a typical telecom company has millions of subscribers and thus generates massive amounts of data. From this aspect, data storage, analysis, and processing are the key concerns. To solve these issues, herein we propose a multilevel cyber-physical social system (CPSS) for the analysis and modeling of large internet data. Our proposed multilevel system has three levels and each level has a specific functionality. Initially, raw Call Detail Data (CDR) was collected at the first level. Herein, the data preprocessing, cleaning, and error removal operations were performed. In the second level, data processing, cleaning, reduction, integration, processing, and storage were performed. Herein, suggested internet activity record measures were applied. Our proposed system initially constructs a graph and then performs network analysis. Thus proposed CPSS system accurately identifies different areas of internet peak usage in a city (Milan city). Our research is helpful for the network operators to plan effective network configuration, management, and optimization of resources. [ABSTRACT FROM AUTHOR]

The integration of cloud and edge computing, along with machine learning, plays a vital role in the development of efficient healthcare systems in smart cities. However, machine and deep learning (DL) models are prone to delayed convergence and Type-I and Type-II errors due to data vastness and high degree imbalance. To overcome the shortcomings of previous frameworks, this work aims to propose an optimization method with DL, 'Deep-Chaotic Nature Whale Optimization' (Deep-CNWO) for early prediction of Blood Pressure disorders among patients under at-home supervision. A simplex search algorithm is integrated to improve the update mechanism of whale optimization algorithm (WOA), thereby creating a CNWO algorithm. The purpose of this hybrid optimization is to increase the accuracy and efficiency of DL models. Leveraging the power of DL and CNWO, this method (Deep-CNWO) provides an effective solution for early detection and proactive management of a chronic disease in at-home healthcare settings. We collected relevant data from clinical studies, including vital signs and patient contextual information, to train and evaluate the deep-CNWO model. The CNWO optimization approach has been used to improve the predictive performance and convergence of DL models. Experiments performed on imbalanced datasets using deep-CNWO have given 99.90% accuracy. The average F-score for emergency cases has improved by 22%, while the average accuracy has increased by 5.72% across all three classes, compared to the results reported in previous related work. Deep-CNWO improves the convergence of DL and reduces Type-I and Type-II errors. The experimental results demonstrate the efficacy of our proposed method for remote patient monitoring and highlight its potential for quick intervention during emergencies. [ABSTRACT FROM AUTHOR]

In the era of ubiquitous computing, the challenges imposed by the increasing demand for real-time data processing, security, and energy efficiency call for innovative solutions. The emergence of fog computing has provided a promising paradigm to address these challenges by bringing computational resources closer to data sources. Despite its advantages, the fog computing characteristics pose challenges in heterogeneous environments in terms of resource allocation and management, provisioning, security, and connectivity, among others. This paper introduces COGNIFOG, a novel cognitive fog framework currently under development, which was designed to leverage intelligent, decentralized decision-making processes, machine learning algorithms, and distributed computing principles to enable the autonomous operation, adaptability, and scalability across the IoT–edge–cloud continuum. By integrating cognitive capabilities, COGNIFOG is expected to increase the efficiency and reliability of next-generation computing environments, potentially providing a seamless bridge between the physical and digital worlds. Preliminary experimental results with a limited set of connectivity-related COGNIFOG building blocks show promising improvements in network resource utilization in a real-world-based IoT scenario. Overall, this work paves the way for further developments on the framework, which are aimed at making it more intelligent, resilient, and aligned with the ever-evolving demands of next-generation computing environments. [ABSTRACT FROM AUTHOR]

Phishing is one of the most important security threats in modern information systems causing different levels of damages to end-users and service providers such as financial and reputational losses. State-of-the-art anti-phishing research is highly fragmented and monolithic and does not address the problem from a pervasive computing perspective. In this survey, we aim to contribute to the existing literature by providing a systematic review of existing experimental phishing research that employs EEG and eye-tracking methods within multi-modal and multi-sensory interaction environments. The main research objective of this review is to examine articles that contain results of at least one EEG-based and/or eye-tracking-based experimental setup within a phishing context. The database search with specific search criteria yielded 651 articles from which, after the identification and the screening process, 42 articles were examined as per the execution of experiments using EEG or eye-tracking technologies in the context of phishing, resulting to a total of 18 distinct papers that were included in the analysis. This survey is approaching the subject across the following pillars: a) the experimental design practices with an emphasis on the applied EEG and eye-tracking acquisition protocols, b) the artificial intelligence and signal preprocessing techniques that were applied in those experiments, and finally, c) the phishing attack types examined. We also provide a roadmap for future research in the field by suggesting ideas on how to combine state-of-the-art gaze-based mechanisms with EEG technologies for advancing phishing research. This leads to a discussion on the best practices for designing EEG and gaze-based frameworks. [ABSTRACT FROM AUTHOR]

The article deploys the material metaphor of "flat surface" to read the unexpected cross-modal affinities between amateurish cover design and professional storytelling in Chinese web novels. Canvassing the ways visual and verbal artifacts put into play metaphoric associations with "flat surface," this essay interprets the popular fascination with flatness as a symptom of China's alignment with ubiquitous computing in its transition to a post-Fordist cultural economy when the world is increasingly conceived as a flat horizon. The author first exhibits a selection of spoof covers and argues that their willful indulgence in two-dimensionality is an antidote to the uncanny similarity of illusionist covers born out of what Mario Carpo calls "mass customization" in the digital era. Then the author turns to examine the web novel Zhuixu 赘婿 (Matrilocal Son-in-Law) by Fennu de Xiangjiao 愤怒的香蕉 (Angry Banana), a highly acclaimed alternate history fiction with a curiously lousy cover, focusing on how Angry Banana's cover designs and narrative storytelling convey the flattening effect. The author attributes the fascination with flatness to the rise of post-Fordism in China's cultural economy by demonstrating how the protagonist in Zhuixu succeeds in flattening "all under heaven" (tianxia 天下) by way of nonhumanizing his existence and weaponizing an internet of things (IoT) while embracing a post-Fordist network of work, life, and governance. Finally, the author furnishes a few more examples to suggest the broader trends toward flatness in Chinese web novels and concludes by discussing the critical edge of the metaphor of flatness in its applicability to post-Fordist cultural analysis. [ABSTRACT FROM AUTHOR]

Big data becomes the key for ubiquitous computing and intelligence, and Distributed Storage Systems (DSS) are widely used in large-scale data centers or in the cloud for efficient data management. However, the data on stored are likely to be unavailable due to hardware failures and cyberattacks, e.g. DDoS. Maximum Distance Separable (MDS) codes are commonly used for the recovery of faulty storage nodes or unavailable data. However, the recovery of data nodes usually involves access to multiple nodes, which introduces significant communication overheads to the DSS. In this paper, a new DSS based on the Redundant Residue Number System (RRNS) is proposed, where efficient recovery is enabled by applying the second version of Chinese Remainder Theorem (CRT-II). The complexity and network traffic of the proposed data protection scheme is analyzed theoretically and compared with that of traditional MDS based DSSs. Experimental results show that the proposed DSS achieves lower encoding complexity, lower recovery complexity and lower network traffic than the MDS based schemes. Although the proposed data protection scheme introduces computation overheads for the case on which there are no failing nodes, its complexity is still lower for scenarios with frequent data updates. In addition, the proposed scheme introduces additional advantages in terms of security and storage flexibility. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless technology and Mobile communication technology worldwide, Mobile Learning represents a new learning trend in the field of higher education, and has gradually become the development trend and research hotspot of independent learning and lifelong learning. Based on modern wireless network mobile communication terminal technology, this paper proposes a Granott model-driven mobile learning system. Firstly, the Granott model is studied and analyzed, and the ubiquitous computing with physical integration and spontaneous interoperation is used to control the Android mobile terminal, which greatly improves the interactivity of M-Learning. Then JSON is selected to optimize the data transmission format in Ajax, and j Query is used to parse JSON data. Ajax + JSON + j Query can shield the complexity of DOM parsing XML files, greatly shorten the data transmission time and improve the efficiency of Granott model. Finally, the Fast kernel density estimation (Fast KDE) algorithm is introduced to compress the data transmitted by Ajax + JSON + j Query, so as to obtain a reasonable number of training samples. Through experiments and relevant data analysis, the proposed mobile learning system based on modern wireless network mobile communication terminal technology, optimized by JSON and Fast KDE algorithm, driven by Granott model, can efficiently complete the learning task, and improve the ubiquity, interactivity, timeliness of mobile learning. [ABSTRACT FROM AUTHOR]

An invisible layer of knowledge is progressively growing with the emergence of situated visualizations and reality-based information retrieval systems. In essence, digital content will overlap with real-world entities, eventually providing insights into the surrounding environment and useful information for the user. The implementation of such a vision may appear close, but many subtle details separate us from its fulfillment. This kind of implementation, as the overlap between rendered virtual annotations and the camera's real-world view, requires different computer vision paradigms for object recognition and tracking which often require high computing power and large-scale datasets of images. Nevertheless, these resources are not always available, and in some specific domains, the lack of an appropriate reference dataset could be disruptive for a considered task. In this particular scenario, we here consider the problem of wine recognition to support an augmented reading of their labels. In fact, images of wine bottle labels may not be available as wineries periodically change their designs, product information regulations may vary, and specific bottles may be rare, making the label recognition process hard or even impossible. In this work, we present augmented wine recognition, an augmented reality system that exploits optical character recognition paradigms to interpret and exploit the text within a wine label, without requiring any reference image. Our experiments show that such a framework can overcome the limitations posed by image retrieval-based systems while exhibiting a comparable performance. [ABSTRACT FROM AUTHOR]

Summary: Human activity recognition is a well‐established research problem in ubiquitous computing. The increased dependency on various smart devices in our daily lives allows us to investigate the sensor data world produced by multimodal sensors embedded in smart devices. However, the raw sensor data are often unlabeled and annotating this vast amount of data are a costly exercise that can often lead to privacy breaches. Self‐supervised learning‐based approaches are at the forefront of learning semantic representation from unlabeled sensor data, including when applied to human activity recognition tasks. As inferring human activity depends on multimodal sensors, addressing the modality difference and inter‐modality dependencies in a model is an important process. This paper proposes a novel self‐supervised learning approach, modality aware contrastive learning (MACL), for representation learning using multimodal sensor data. The approach uses different sensing modalities to create different views of an input signal. Thus, the model is able to learn the representations by maximizing the similarity among different sensing modalities of the same input signal. Extensive experiments were performed on four publicly available human activity recognition data sets to verify the effectiveness of our proposed MACL method. The experimental evaluation results show that the MACL method attains a comparable performance for human activity recognition to the compared baseline models, directly exceeding the performance of models using standard augmentation transformation strategies. [ABSTRACT FROM AUTHOR]

Augmented reality provides users with immersive digital experiences by overlaying digital content on top of physical world and helps users better understand reality. In recent years, research on context awareness in augmented reality has received widespread attention. Augmented reality is closely related to its usage context, not only it is basic contextual information such as location information, light level, head posture, but also higher level contextual information such as geometric information, semantic information and eye movement information, these contextual information have impacts on user experience of augmented reality in various degrees. Based on recent advances in context awareness of augmented reality, this paper outlines the contextual factors of augmented reality, illustrates the context-aware processing flow in augmented reality, and analyzes the context-aware algorithms and latest applications related to geometry, semantics and eye movement contextual information in augmented reality. Finally, combined with the current status of augmented reality contextual awareness, it discusses the future development trends of augmented reality contextual awareness, so as to provide reference for future research. [ABSTRACT FROM AUTHOR]

The ubiquitous adoption of Android devices has unfortunately brought a surge in malware threats, compromising user data, privacy concerns, and financial and device integrity, to name a few. To combat this, numerous efforts have explored automated botnet detection mechanisms, with anomaly-based approaches leveraging machine learning (ML) gaining attraction due to their signature-agnostic nature. However, the problem lies in devising accurate ML models which capture the ever evolving landscape of malwares by effectively leveraging all the possible features from Android application packages (APKs).This paper delved into this domain by proposing, implementing, and evaluating an image-based Android malware detection (AMD) framework that harnessed the power of feature hybridization. The core idea of this framework was the conversion of text-based data extracted from Android APKs into grayscale images. The novelty aspect of this work lied in the unique image feature extraction strategies and their subsequent hybridization to achieve accurate malware classification using ML models. More specifically, four distinct feature extraction methodologies, namely, Texture and histogram of oriented gradients (HOG) from spatial domain, and discrete wavelet transform (DWT) and Gabor from the frequency domain were employed to hybridize the features for improved malware identification. To this end, three image-based datasets, namely, Dex, Manifest, and Composite, derived from the information security centre of excellence (ISCX) Android Malware dataset, were leveraged to evaluate the optimal data source for botnet classification. Popular ML classifiers, including naive Bayes (NB), multilayer perceptron (MLP), support vector machine (SVM), and random forest (RF), were employed for the classification task. The experimental results demonstrated the efficacy of the proposed framework, achieving a peak classification accuracy of 93.03% and recall of 97.1% for the RF classifier using the Manifest dataset and a combination of Texture and HOG features. These findings validate the proof-of-concept and provide valuable insights for researchers exploring ML/deep learning (DL) approaches in the domain of AMD. [ABSTRACT FROM AUTHOR]

People's interaction with computers has been explained through several theories and methods throughout the history of computers and their uses; more complex scenarios of ubiquitous and pervasive computing experimented nowadays invite other ways of considering human-technology interaction. The enactive approach to cognition, centered in human experience in the environment, has shown itself as a possibility to understand interaction in these new scenarios. In this paper we present a view of interaction, named socioenactive, to explain our relation with contemporary computational technologies in scenarios of ubiquitous and pervasive systems, understood from the enactivist perspective. After presenting core concepts related to intersubjectivity that helped to build up what we mean by socioenactive interaction, we illustrate it with some scenarios of ubiquitous and pervasive technology (situated in a school, a museum and a hospital), designed for the study of the phenomena of interaction. Graspable aspects of intersubjectivity are raised in the considered scenarios and affordances for the socioenactive interaction are illustrated. Implications of the study are discussed leading to aspects to consider in a research agenda in the field. Interaction with technology involves the body's physical, emotional, and social aspects; Human-technology relation presupposes a mutual specification in and with the environment; Intersubjective aspects must be addressed in the design of ubiquitous environments; Socioenactive interaction presupposes a social-physical-digital tripartite coupling; Socioenactive systems are materialized in school, museum and hospital scenarios. [ABSTRACT FROM AUTHOR]

In today's fast‐paced society, the Internet of Things (IoT) is revolutionizing businesses by bridging the gap between the digital and physical realms. This transformative technology enables rapid changes in the way people conduct business. Its fundamental aim is to connect everything in our world—objects, individuals, locations, and processes—under a unified infrastructure, providing valuable information and control over the state of our environment. The IoT represents a novel approach to developing diverse and distributed systems, which have now evolved into a pervasive computing cloud infrastructure. However, due to limitations in computing power and storage resources dedicated to handling the vast amounts of IoT data, a cloud‐based architecture is often employed in the IoT realm. Unfortunately, this shift has introduced a range of complex security and trust concerns within the cloud‐based IoT environment. As the demand for widespread deployment of cloud‐based IoT continues to soar, significant security‐related threats have emerged, prompting numerous researchers to develop cryptographic algorithms aimed at enhancing the security of both the Cloud and IoT. This paper provides an overview of cloud‐based IoT technology and seeks to fulfill the primary objective of conducting a survey on encryption algorithms in the context of the cloud and IoT. The research delves into the strategies, challenges, and variations associated with encryption in cloud and IoT systems. Additionally, this work includes a comparative analysis of the computational complexity of these techniques. [ABSTRACT FROM AUTHOR]

The 'Three Good Things' (TGT), a structured journaling exercise, has shown great effect in helping improve mental well-being. However, the exercise could be quite tedious and difficult to carry out daily. To improve the user experience of the exercise, we developed 'Happy Click', a mobile application which makes use of a physically present smart button to enable users to record their daily TGT in a more engaging manner. Two preliminary exploratory studies were carried out to examine the feasibility of our approach where the effects of the TGT intervention designed around the mobile and tangible happy click concepts were examined. The findings suggested that participants had significantly less stress after carrying out the TGT using the tangible interface. Participants also reported better user experience and acceptance when carrying out the exercise using the tangible interface compared to the mobile and higher levels of attractiveness and stimulation with the mobile TGT compared to the traditional approach. While these promising results should be interpreted with caution due to the limited sample size, they also underscore the potential advantages of our 'Happy click' approach and highlight the need for further, larger-scale evaluations to be conducted to validate its efficacy and scalability. [ABSTRACT FROM AUTHOR]

In the world of wearable technology, smart homes and cities, innovation continues to thrive. Computing is pervasive, and ambient computing is ubiquitous integration of computer into the background of everyday environments and situations. With contact-free sensors and wearables that require interaction, ambient computing is increasingly finding use in the healthcare industry. This ubiquitous nature of ambient computing raises concerns on privacy, confidentiality, data protection, informed consent and fairness. The continuous collection of massive health care data not only unobtrusively monitors the health condition of the patient and trajectory for high quality care but also has a detrimental effect in terms of privacy, bias and fairness. This paper discusses the main ethical issues in ambient health care systems and explores the blockchain based solutions for ambient health care systems emphasizing on patient dignity and autonomy. Furthermore, a secure EHR access using RBAC and Cryptographic techniques for the responsible development and use of ambient health care systems is proposed in this research work. [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]

In the contemporary energy landscape, characterized by a global commitment to sustainability, the effective management and forecasting of energy consumption play pivotal roles in achieving environmental and economic goals. As nations strive to meet sustainable development targets, optimizing energy use becomes imperative. This paper addresses these challenges by focusing on load forecasting and energy management within the context of a Savona campus microgrid. In this thesis, the NNR algorithm based load profile prediction model was proposed. The development process involved a detailed exploration of the correlation between weather information and electricity consumption. Furthermore, the outputs of the load forecasting model, namely the predicted load profiles, were subsequently utilized in the Energy Management System (EMS) to optimally manage power flows in the campus microgrid using an Internet of things as a service (IoTaaS) with day-ahead forecasting model. The overall results of the model evaluation across all periods reveal a Mean Absolute Error (MAE) of 9.63 kW, a Coefficient of Determination (R2) of 0.79, and a Mean Absolute Percentage Error (MAPE) of 9.02%. These metrics provide a comprehensive assessment of the model's performance across various temperature conditions. The proposed load profile forecasting model was integrated into the Energy Management System (EMS) developed for Savona campus microgrid in Italy. The findings provide a valuable framework for optimizing microgrid operations, aligning with global sustainability objectives. [ABSTRACT FROM AUTHOR]

Generally speaking, we can easily specify many causal relationships in the prediction tasks of ubiquitous computing, such as human activity prediction, mobility prediction, and health prediction. However, most of the existing methods in these fields failed to take advantage of this prior causal knowledge. They typically make predictions only based on correlations in the data, which hinders the prediction performance in real-world scenarios, because a distribution shift between training data and testing data generally exists. To fill in this gap, we proposed a Generative Adversarial Network (GAN)-based Causal Information Learning prediction framework, which can effectively leverage causal information to improve the prediction performance of existing ubiquitous computing deep learning models. Specifically, faced with a unique challenge that the treatment variable, referring to the intervention that influences the target in a causal relationship, is generally continuous in ubiquitous computing, the framework employs a representation learning approach with a GAN-based deep learning model. By projecting all variables except the treatment into a latent space, it effectively minimizes confounding bias and leverages the learned latent representation for accurate predictions. In this way, it deals with the continuous treatment challenge, and in the meantime, it can be easily integrated with existing deep learning models to lift their prediction performance in practical scenarios with causal information. Extensive experiments on two large-scale real-world datasets demonstrate its superior performance over multiple state-of-the-art baselines. We also propose an analytical framework together with extensive experiments to empirically show that our framework achieves better performance gain under two conditions: when the distribution differences between the training data and the testing data are more significant and when the treatment effects are larger. Overall, this work suggests that learning causal information is a promising way to improve the prediction performance of ubiquitous computing tasks. We open both our dataset and code1 and call for more research attention in this area. [ABSTRACT FROM AUTHOR]

In the realm of architectural evolution, the paradigm of static built environments has progressively shifted towards adaptive, responsive, and intelligent spaces. This paper presents a pioneering government-funded applied research project, the 'Spatially Intelligent Arts Centre', which exemplifies the convergence of multiple disciplines in the pursuit of redefining the traditional notion of a static arts centre. This endeavour sought to transform an existing arts centre building into a dynamic and intelligent space, thereby redefining its agency to foster novel user experiences and optimize operational efficiency. The project transcended conventional boundaries by merging the expertise of architects, computer scientists, user experience designers, spatial computing specialists, technology developers, and interface designers. Through transdisciplinary collaboration and adopting a design-thinking methodology, the project not only challenges the conventional limitations of architectural design but also offers a tangible illustration of the burgeoning field of human-building interaction research and practice. This paper details the conceptual underpinnings, technical implementations, and experiential outcomes of the Spatially Intelligent Arts Centre project, underscoring its significance as a model for implementation of human-building interaction in the context of a cultural building. [ABSTRACT FROM AUTHOR]

The article explores the concept of ubiquitous learning, which involves integrating digital technology into education. It highlights how technology has made learning more accessible and convenient. Ubiquitous learning challenges traditional educational models by empowering learners, recognizing individual differences, and promoting collaboration. The article acknowledges that technology alone does not guarantee effective learning and emphasizes the importance of teachers possessing higher order skills to create inclusive learning environments. It suggests that embracing technological advancements may lead to a new educational paradigm and encourages educators to take a leading role in innovation. The article concludes by emphasizing the need for breakthrough practices and technologies to reshape teaching and learning. [Extracted from the article]

The seamless integration of technology for computing into everyday items and environments is known as pervasive computing. To protect against cyber threats and vulnerabilities, robust security mechanisms are necessary. Conventional security measures, including gateways and the use of encryption, may not be sufficient to address the unique challenges encountered in ubiquitous computing systems. But these techniques are still vital. In addition to the variety of devices, resource limitations, mobility needs, and the possibility of large-scale distributed attacks, these obstacles also include the potential for attack. Network virtualization, that abstracts and separates network facilities and functions, is a promising way to increasing security in pervasive computing deployments: it abstracts and isolates network resources and processes. Wireless communication play a significant part in the development of a digital infrastructure that is both resilient and trustworthy. The processes of dynamic resource allocation, isolation, and management of network bandwidth are made possible through the utilization of virtualization, leads to the proposal of Secure Wireless Virtual Resource Allocation and Authentication Algorithm(SWVRA3) to make the abstraction of the network's physical resources into virtualized entities By using network virtualization, pervasive computing applications and services can be secured with logically segregated virtual networks. The cross-contamination and security breaches can be reduced by this separation. Furthermore, flexible configuration, dynamic allocation of resources, and centralized virtual control are allowed by network visualization that improves threat incidence response, enforcement of policies, and security surveillance. [ABSTRACT FROM AUTHOR]

The coordination and organization of economic activity has always been one of the greatest challenges of humanity. For a long time, centralized models have been the dominant approach, but the emergence of technologies such as the internet, open-source software, peer-to-peer and ubiquitous computing, smart contracts and blockchain is leading to a shift toward decentralization. This trend is now culminating with a new phase of innovation called Decentralized Autonomous Organization ("DAO"). DAOs offer the potential to empower communities, promote decentralization and reliable coordination, and increase transparency and meritocracy in corporate governance using the institutional technology that is blockchain. In this paper, we present a systematic literature review of 195 academic publications on the DAO phenomenon using the PRISMA methodology. We aim to answer the following research questions: What are the characteristics of DAOs? What topics related to DAOs were researched by whom? Why do DAOs show great potential? When do DAOs face limitations? Where can solutions be found for the identified limitation? We also propose a research agenda and suggest future research directions. [ABSTRACT FROM AUTHOR]

The expanding computer landscape leads us toward ubiquitous computing, in which smart gadgets seamlessly provide intelligent services anytime, anywhere. Smartphones and other smart devices with multiple sensors are at the vanguard of this paradigm, enabling context-aware computing. Similar setups are also known as smart spaces. Context-aware systems, primarily deployed on mobile and other resource-constrained wearable devices, use a variety of implementation approaches. Rule-based reasoning, noted for its simplicity, is based on a collection of assertions in working memory and a set of rules that regulate decision-making. However, controlling working memory capacity efficiently is a key challenge, particularly in the context of resource-constrained systems. The paper’s main focus lies in addressing the dynamic working memory challenge in memory-constrained devices by introducing a systematic method for content removal. The initiative intends to improve the creation of intelligent systems for resource-constrained devices, optimize memory utilization, and enhance context-aware computing. [ABSTRACT FROM AUTHOR]

Despite its rich history of success in controlling powered prostheses and emerging commercial interests in ubiquitous computing, myoelectric control continues to suffer from a lack of robustness. In particular, EMG-based systems often degrade over prolonged use resulting in tedious recalibration sessions, user frustration, and device abandonment. Unsupervised adaptation is one proposed solution that updates a model's parameters over time based on its own predictions during real-time use to maintain robustness without requiring additional user input or dedicated recalibration. However, these strategies can actually accelerate performance deterioration when they begin to classify (and thus adapt) incorrectly, defeating their own purpose. To overcome these limitations, we propose a novel adaptive learning strategy, Context-Informed Incremental Learning (CIIL), that leverages in situ context to better inform the prediction of pseudo-labels. In this work, we evaluate these CIIL strategies in an online target acquisition task for two use cases: (1) when there is a lack of training data and (2) when a drastic and enduring alteration in the input space has occurred. A total of 32 participants were evaluated across the two experiments. The results show that the CIIL strategies significantly outperform the current state-of-the-art unsupervised high-confidence adaptation and outperform models trained with the conventional screen-guided training approach, even after a 45-degree electrode shift (p < 0.05). Consequently, CIIL has substantial implications for the future of myoelectric control, potentially reducing the training burden while bolstering model robustness, and leading to improved real-time control. [ABSTRACT FROM AUTHOR]

In recent years, the continuous progress of wireless communication and sensor technology has enabled sensors to be better integrated into mobile devices. Therefore, sensor-based Human Activity Recognition (HAR) has attracted widespread attention among researchers, especially in the fields of wearable technology and ubiquitous computing. In these applications, mobile devices' built-in accelerometers and gyroscopes have been typically used for human activity recognition. However, devices such as smartphones were placed in users' pockets and not fixed to their bodies, and the resulting changes in the orientation of the sensors due to users' habits or external forces can lead to a decrease in the accuracy of activity recognition. Unfortunately, there is currently a lack of publicly available datasets specifically designed to address the issue of device angle change. The contributions of this study are as follows. First, we constructed a dataset with eight different sensor placement angles using accelerometers and gyroscopes as a prerequisite for the subsequent research. Second, we introduced the Madgwick algorithm to extract quaternion mode features and alleviate the impact of angle changes on recognition performance by fusing raw accelerometer data and quaternion mode features. The resulting study provides a comprehensive analysis. On the one hand, we fine-tuned ResNet and tested its stability on our dataset, achieving a recognition accuracy of 97.13%. We included two independent experiments, one for user-related scenarios and the other for user-independent scenarios. In addition, we validated our research results on two publicly available datasets, demonstrating that our method has good generalization performance. [ABSTRACT FROM AUTHOR]

In the era of ubiquitous computing with flourished visual displays in our surroundings, the application of haptic feedback technology still remains in its infancy. Bridging the gap between haptic technology and the real world to enable ambient haptic feedback on various physical surfaces is a grand challenge in the field of human-computer interaction. This paper presents the concept of an active electronic skin, characterized by three features: richness (multi-modal haptic stimuli), interactivity (bi-directional sensing and actuation capabilities), and invisibility (transparent, ultra-thin, flexible, and stretchable). By deploying this skin on physical surfaces, dynamic and versatile multi-modal haptic display, as well as tactile sensing, can be achieved. The potential applications of this skin include two categories: skin for the physical world (such as intelligent home, intelligent car, and intelligent museum), and skin for the digital world (such as haptic screen, wearable device, and bare-hand device). Furthermore, existing skin-based haptic display technologies including texture, thermal, and vibrotactile feedback are surveyed, as well as multidimensional tactile sensing techniques. By analyzing the gaps between current technologies and the goal of ambient haptics, future research topics are proposed, encompassing fundamental theoretical research on the physiological and psychological perception mechanisms of human skin, spatial-temporal registration among multimodal haptic stimuli, integration between sensing and actuation, and spatial-temporal registration between visual and haptic display. This concept of active electronic skin is promising for advancing the field of ambient haptics, enabling seamless integration of touch into our digital and physical surroundings. [ABSTRACT FROM AUTHOR]

In elite sports, athletic excellence demands meticulous performance preparation and a sound health status. This paper overviews the current propositions and applications of pervasive computing and data analytics and our vision on how they should be used in future frameworks to contribute to the optimal balance of athletes' performance and health requirements. Two main areas will be discussed. The first area is Sports Performance Optimization, in which we consider interesting recent advancements in data analytics for performance improvement, equipment design, and team member recruitment and selection. We will also briefly discuss how the betting industry has been relaying and developing sports analytics. The second area is Athlete's Wellness and Wellbeing, which will discuss how wearables and data analytics have been used to assess physical activity and sedentary behavior profiles, sleep and circadian rhythm, nutrition and eating behavior, menstrual cycles, and training/performance readiness. In the final part of this paper, we argue that a critical issue for managers to enhance their decision making is the standardization of acquired information and decision-making processes, while introducing an adaptable, personalized approach. Thus, we present and discuss new theoretical and practical approaches that could potentially address this problem and identify precision medicine as a recommended methodology. This conceptualization involves the integration of pervasive computing and data analytics by employing predictive models that are constantly updated with the outcomes from monitoring tools and athletes' feedback interventions. This framework has the potential to revolutionize how athletes' performance and well-being are monitored, assessed, and optimized, contributing to a new era of precision in sports science and medicine. [ABSTRACT FROM AUTHOR]

In Portugal, more than 98% of domestic cooking oil is disposed of improperly every day. This avoids recycling/reconverting into another energy. Is also may become a potential harmful contaminant of soil and water. Driven by the utility of recycled cooking oil, and leveraging the exponential growth of ubiquitous computing approaches, we propose an IoT smart solution for domestic used cooking oil (UCO) collection bins. We call this approach SWAN, which stands for Smart Waste Accumulation Network. It is deployed and evaluated in Portugal. It consists of a countrywide network of collection bin units, available in public areas. Two metrics are considered to evaluate the system's success: (i) user engagement, and (ii) used cooking oil collection efficiency. The presented system should (i) perform under scenarios of temporary communication network failures, and (ii) be scalable to accommodate an ever-growing number of installed collection units. Thus, we choose a disruptive approach from the traditional cloud computing paradigm. It relies on edge node infrastructure to process, store, and act upon the locally collected data. The communication appears as a delay-tolerant task, i.e., an edge computing solution. We conduct a comparative analysis revealing the benefits of the edge computing enabled collection bin vs. a cloud computing solution. The studied period considers four years of collected data. An exponential increase in the amount of used cooking oil collected is identified, with the developed solution being responsible for surpassing the national collection totals of previous years. During the same period, we also improved the collection process as we were able to more accurately estimate the optimal collection and system's maintenance intervals. [ABSTRACT FROM AUTHOR]