Miracle-dz/UniFAR-FTUs huggingface dataset:
Task Caching, Offloading, and Resource Allocation in D2D-Aided Fog Computing Networks

In this paper, we investigate the allocation of resource in D2D-aided Fog computing system with multiple mobile user equipments (MUEs). We consider each MUE has a request for task from a task library and needs to make a decision on task performing with a selection of three processing modes which include local mode, fog offloading mode, and cloud offloading mode. Two scenarios are considered in this paper, which mean task caching and its optimization in off-peak time, task offloading, and its optimization in immediate time. In particular, task caching refers to cache the completed task application and its related data. In the first scenario, to maximize the average utility of MUEs, a task caching optimization problem is formulated with stochastic theory and is solved by a GA-based task caching algorithm. In the second scenario, to maximize the total utility of system, the task offloading and resource optimization problem is formulated as a mixed integer nonlinear programming problem (MINLP) with a joint consideration of the MUE allocation policy, task offloading policy, and computational resource allocation policy. Due to the nonconvex of the problem, we transform it into multi-MUEs association problem (MMAP) and mixed Fog/Cloud task offloading optimization problem (MFCOOP). The former problem is solved by a Gini coefficient-based MUEs allocation algorithm which can select the most proper MUEs who contribute more to the total utility. The task offloading optimization problem is proved as a potential game and solved by a distributed algorithm with Lagrange multiplier. At last, the simulations show the effectiveness of the proposed scheme with the comparison of other baseline schemes.
Safe and Efficient Intelligent Intersection Control of Autonomous Vehicles

In this dissertation, we address a problem of safe and efficient intersection crossing traffic management of autonomous and connected ground traffic. Toward this objective, we propose several algorithms to handle different traffic environments. First, an algorithm that is called the Discrete-time occupancies trajectory (DTOT) based Intersection traffic Coordination Algorithm (DICA) is proposed. All vehicles in the system are Connected and Autonomous Vehicles (CAVs) and capable of wireless Vehicle-to-Intersection communication. The main advantage of DICA is that it enables us to utilize the intersection space more efficiently resulting in less delay for vehicles to cross the intersection. In the proposed framework, an intersection coordinates the motions of CAVs based on their proposed DTOTs to let them cross the intersection efficiently while avoiding collisions. In case when there is a potential collision between vehicles’ DTOTs, the intersection modifies conflicting DTOTs to avoid the collision and requests CAVs to approach and cross the intersection according to the modified DTOTs. We also prove that the basic DICA is deadlock free and starvation free. We show that the basic DICA has a computational complexity of O(nLm) where n is the number of vehicles granted to cross an intersection and Lm is the maximum length of intersection crossing routes. To improve the overall computational efficiency of the algorithm, the basic DICA is enhanced by several computational techniques. The enhanced algorithm has a reduced computational complexity of O(nLm log2 Lm). The problem of evacuating emergency vehicles as quickly as possible through autonomous and connected intersection traffic is also addressed in this dissertation. The proposed Reactive DICA aims to determine an efficient vehicle-passing sequence
A Hybrid Deep Learning-Based Model for Anomaly Detection in Cloud Datacenter Networks

With the emergence of the Internet-of-Things (IoT) and seamless Internet connectivity, the need to process streaming data on real-time basis has become essential. However, the existing data stream management systems are not efficient in analyzing the network log big data for real-time anomaly detection. Further, the existing anomaly detection approaches are not proficient because they cannot be applied to networks, are computationally complex, and suffer from high false positives. Thus, in this paper a hybrid data processing model for network anomaly detection is proposed that leverages grey wolf optimization (GWO) and convolutional neural network (CNN). To enhance the capabilities of the proposed model, GWO and CNN learning approaches were enhanced with: 1) improved exploration, exploitation, and initial population generation abilities and 2) revamped dropout functionality, respectively. These extended variants are referred to as Improved-GWO (ImGWO) and Improved-CNN (ImCNN). The proposed model works in two phases for efficient network anomaly detection. In the first phase, ImGWO is used for feature selection in order to obtain an optimal trade-off between two objectives, i.e., reduced error rate and feature-set minimization. In the second phase, ImCNN is used for network anomaly classification. The efficacy of the proposed model is validated on benchmark (DARPA’98 and KDD’99) and synthetic datasets. The results obtained demonstrate that the proposed cloud-based anomaly detection model is superior in comparison to the other state-of-the-art models (used for network anomaly detection), in terms of accuracy, detection rate, false positive rate, and F-score. In average, the proposed model exhibits an overall improvement of 8.25%, 4.08%, and 3.62% in terms of detection rate, false positives, and accuracy, respectively; relative to standard GWO with CNN.
Factors Affect Knowledge Sharing by Using Knowledge Management Systems to Support Decision Making Processes

In the current fluid environment, the challenge for society and organizational decision makers is how to accumulate and share knowledge that stems from various sources, and maximize value generated from all available assets. For this purpose, this paper explores how knowledge sharing (KS) is mediated by technology, and more specifically, by Knowledge Management Systems (KMSs) to support decision making processes (DMP) in organizations. In this study, 42 semi-structured interviews were conducted and a set of strong overarching themes concerning the factors that affect KS in using KMSs to support the DMP in Multinational Corporations (MNCs) in a conceptual framework which comprises four core themes: Knowledge Management Systems, Knowledge Sharing Practice, Culture, and Decision-making Processes. This study proposes a conceptual framework that would help managers, employees and decision makers to identify ways of leveraging and sharing knowledge by using KMSs in MNCs.
Long Range Low Power Wireless Communication Technologies for the IoT

The Internet of Things addresses a huge set of possible application domains, requiring both short- and long-range communication technologies. When long distances are present, a number of proprietary and standard solutions for Low Power Wide Area Networks are already available. Among them, LoRaWAN and NB-IoT are candidate technologies supported by many network operators. LoRaWAN is one of the first technologies defined to operate in unlicensed bands. Its simple access protocol is designed to avoid complexity and costs while maximising the transmission range. The proprietary modulation used is very robust with respect to the interferers present in the shared bands used. NB-IoT is a new radio access technology targeting a large set of use cases for massive machine-type communications standardised by the 3GPP. NB-IoT has been enhanced in terms of coverage and power saving capabilities while reducing the complexity at the same time. In this thesis, first, many typical applications that may benefit from these technologies are presented, with a focus on the performance metrics and the definition of the scenario and traffic pattern. Secondly, the LoRaWAN technology is assessed both experimentally and through simulations, to characterise it from the link-level and system-level viewpoint, with the target of estimating the capacity of a LoRaWAN gateway and a multi-gateway network to serve a large area. Then, this thesis provides an overview of NB-IoT, together with a mathematical model of the network able to predict the maximum performance in a given scenario with a specific configuration of some design parameters. This model is used to study how these parameters affect the overall performance and how the optimal configuration may be chosen according to arbitrary criteria. Finally, some projects and practical activities are presented to prove the need for these standards, and to share the know-how that was developed during these studies.
Adaptive Traffic Signal Control Mechanism for Intelligent Transportation Based on a Consortium Blockchain

The development of vehicular ad-hoc networks (VANETs) has facilitated adaptive traffic signal control for intelligent transportation. In this paper, we proposed the traffic signal control mechanism based on a consortium blockchain, which has saved plenty of financial and material resources. It has solved the centralization problems and minimized the high degree of human intervention in the process of traffic signal light management. As a road is congested, the vehicle forwards road condition messages. The traffic department ( $TD$ ) adjusts the signal light duration to allow the synergistic optimization management, and control the traffic vehicle status through a smart contract. In addition, we propose a credibility mechanism to effectively prevent vehicles from broadcasting mendacious messages and malicious requests, thereby enhancing the credibility of vehicles and providing a secure and trustworthy communication environment for the VANETs. It is hazardous for vehicles to send plaintext messages in an open environment because their privacy and security are threatened. Thus, we utilize ElGamal encryption and group signature algorithm to guarantee the confidentiality, privacy, and non-repudiation of any information. The safety analysis and performance evaluation demonstrate that the scheme is feasible and valid, and it can facilitate the adaptive control of traffic signal lights.
Exploiting Parallelism and Vectorisation in Breadth-First Search for the Intel Xeon Phi

Modern applications generate massive amounts of data that is challenging to process or analyse. Graph algorithms have emerged as a solution for the analysis of such data because they can represent the entities participating in the generation of large-scale datasets in terms of vertices and their relationships in terms of edges. Graph analysis algorithms are used for finding patterns within these relationships, aiming to extract information to be further analysed. The breadth-first search (BFS) is one of the main graph search algorithms used for graph analysis and its optimisation has been widely researched using different parallel computers. However, the parallelisation of BFS has been shown to be challenging because of its inherent characteristics, including irregular memory access patterns, data dependencies and workload imbalance, that limit its scalability. This paper investigates the optimisation of the BFS on the Xeon Phi (Knights Corner), a modern parallel architecture provided with an advanced vector processor supporting the AVX-512 instruction set, using a bespoke development framework integrated with the Graph 500 benchmark. In addition, to demonstrate portability, we show results for a direct port of the algorithms to a more recent version of the Xeon Phi (Knights Landing) and to a Skylake CPU which supports most of the AVX-512 instruction set. Optimised parallel versions of two high-level algorithms for BFS were created using vectorisation, starting with the conventional top-down BFS algorithm and, building on this, a hybrid BFS algorithm. On the KNC our best implementations result in speedups of 1.37x (top-down) and 1.37x (hybrid), for a one million vertices graph, compared to the state-of-the-art. On the KNL and Skylake, the performance is higher than on KNC. In addition, we show results of our best hybrid algorithm on real-world graphs from the SNAP datasets with speedups up to 1.3x on KNC. Performance on KNL and Skylake is again higher, demonstrating the robustness and portability of our algorithm. The hybrid BFS algorithm can be further used to speed up other graph analysis algorithms and the lessons learned from vectorisation can be applied to other algorithms targeting existing and future models of the Xeon Phi and other advanced vector architectures.
A quasi-experimental study into the influence of a short-term positive psychological intervention on social anxiety, gratitude and happiness in undergraduates

The introduction of Positive Psychological Interventions (PPI’s) has been recognised to represent the ethos of the relatively novel area of Positive Psychology, regarding the value of developing positive individual traits. Numerous PPI’s have been praised for their ability to influence an individual’s experience of gratitude and happiness and reducing social anxiety. Research has suggested that focussing on these traits in undergraduates have shown benefits in academic achievement and social and moral development, therefore justifying investigating the use of the short-term ‘Three Good Things,’ intervention for influence on these variables. On completion of pre-intervention measures of social anxiety, gratitude and happiness, forty-six undergraduate students were randomly allocated to a short-term ‘Three Good Things’ intervention or an active control group, where they were required to complete the corresponding activity for one week. A series of 2x2 mixed ANOVA’s were conducted, where despite observation of positive changes in happiness and gratitude, and negative changes in social anxiety for participants in the experimental group from pre to post-intervention, all interactions were non-significant. This limits implications for the use of the ‘Three Good Things’ task on influencing these variables. However, various moderators of PPI’s have been recognised for their influence on the effectiveness of these methods, thus proposing the need for further research.
Dual Encoder Classifier Models as Constraints in Neural Text Normalization

Neural text normalization systems can achieve low error rates; however, the errors they make include not only ones from which the hearer can recover (such as reading $3 as three dollar) but also unrecoverable errors, such as reading $3 as three euros. FST decoding constraints have proven effective at reducing unrecoverable errors. In this paper we explore an alternative approach to error mitigation: using dual encoder classifiers trained with both positive and negative examples to implement soft constraints on acceptability. Since the error rates are very low, it is difficult to determine when improvement is significant, but qualitative analysis suggests that soft dual encoder constraints can help reduce the number of unrecoverable errors.
Effect of well number on the performance of quantum-well solar cells

The effect of increasing the number of quantum wells in a strain-compensated, multiquantum-well solar cell is investigated. It is found that as the well number is increased, dark current level close to the operating point rises linearly. Short-circuit current in the AM0 spectrum also rises linearly with the inclusion of more quantum wells. This allows the cell to maintain a constant open-circuit voltage irrespective of the number of wells grown. This is anticipated to have advantages when the cell is used as a replacement for the GaAs junction in the existing generation of tandem and triple-junction cells since current levels can be matched to the upper junction without detriment to the voltage performance. This result allows us to predict a tandem cell AM0 efficiency of 23.8% based on the 50-well cell.
Outflow-Infall Interactions in Early Star Formation and Their Impact on the Mass-assembling Process in L1228

New high-resolution molecular line and continuum millimeter interferometer observations of the environment within 104 AU of the low-mass protostar IRAS 20582+7724, in the L1228 dark cloud, enable a detailed study of the molecular outflow near the protostellar source, the circumstellar envelope, and the outflow-envelope interaction. In 12CO and 13CO maps a wide-angle molecular outflow has been imaged to within less than 1000 AU of the 2.7 mm continuum emission associated with IRAS 20582+7724. The morphology and kinematics are consistent with entrainment of the gas by an underlying wind that has both a wide-angle and a collimated component. The HCO+ (1-0), HNC (1-0), and CS (2-1) lines are sensitive to infall motions and trace an elongated circumstellar envelope perpendicular to the outflow axis. Together the morphology, kinematics, and energetics of both the outflow and infalling circumstellar envelope suggest that the outflow has excavated wide-angle cavities in the envelope and thereby constrained the infall region to a limited volume outside the outflow lobes. In the IRAS 20582+7724 system, outflow-envelope interactions evidently have an important effect on the mass-assembling process. In addition, our multimolecular line data show that the outflow has affected the chemical composition of the gaseous environment surrounding the protostar, through the shock enhancement of different molecular species.
Level-Set-XFEM-Density Topology Optimization of Active Structures: Methods and Applications

To unlock the potential of advanced manufacturing technologies like additive manufacturing, an inherent need for sophisticated design tools exists. In this thesis, a systematic approach for designing printed active structures using a combined level-set (LS) extended finite element (XFEM) density topology optimization (TO) scheme is developed. This combined scheme alleviates the downsides of both LS and density based TO approaches while building upon the advantages of either method. Thus, a superior design optimization approach is created, which, when coupled with the XFEM, yields a highly accurate physical modeling method. The unique capabilities of this combined approach include hole nucleation and minimum feature size control while retaining a crisp and unambiguous definition of the material interface. Different stabilization and regularization schemes are developed to maximize the robustness of the proposed method. Ensuring sufficient numerical stability during the TO process is especially critical when using large deformation nonlinear elasticity models. Without sufficient stabilization, divergence in the analysis or optimization process is frequently encountered. Therefore, a novel explicit LS regularization scheme, based on the construction of a signed distance field (SDF) for every design iteration, is developed in this thesis. It is also demonstrated that the obtained SDF can be used for minimum feature size control and control of the mean curvature during a TO process. Numerical design examples in 2D and 3D are presented to demonstrate the applicability of the proposed combined TO method. Physical specimens of 4D printed samples are used to validate the accuracy of the predicted structural performance by the developed thermomechanical large-strain XFEM model. Finally, conclusions and recommendations for future work are presented and the original contributions made in this thesis are summarized.
Music-search behaviour on a social Q&A site: A cross-gender comparison

While there have been numerous studies of music-search behaviour, little is known about gendered aspects of how it is carried out on social question and answer sites. The article examines gender differences manifested on one such site with regard to (a) the motivations of the person posing the question, (b) intervening variables that influence music-search behaviour and (c) the formulation of the questions. Results from manual categorisation and other analysis of 17,380 music-relevant questions collected from the site show that males who asked questions did so more often, provided more answers and had more followers than female question-posters. Males tended to include music context information in questions asking for ready reference, whereas females often asked questions in a second-person pronoun aiming for promoting discussion. Such research results add to the current understanding of music-search behaviour and contribute new insights that can inform development of better music services/systems.
PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing

This paper presents PointWeb, a new approach to extract contextual features from local neighborhood in a point cloud. Unlike previous work, we densely connect each point with every other in a local neighborhood, aiming to specify feature of each point based on the local region characteristics for better representing the region. A novel module, namely Adaptive Feature Adjustment (AFA) module, is presented to find the interaction between points. For each local region, an impact map carrying element-wise impact between point pairs is applied to the feature difference map. Each feature is then pulled or pushed by other features in the same region according to the adaptively learned impact indicators. The adjusted features are well encoded with region information, and thus benefit the point cloud recognition tasks, such as point cloud segmentation and classification. Experimental results show that our model outperforms the state-of-the-arts on both semantic segmentation and shape classification datasets.
Efficient privacy‐preserving scheme supporting disjunctive multi‐keyword search with ranking

Information storage and retrieval from the cloud is growing continuously unabated. The cost‐efficient solutions offered by the cloud providers to the end‐users have motivated them to outsource their confidential data to the cloud. Outsourcing confidential data leads to enhanced privacy risks due to disclosure of sensitive information to adversaries. To handle this disclosure of information, encryption is preferred, but it hinders the efficient searching on the documents. The existing searchable encryption schemes either focused on optimization of search time or improvement of search efficiency. To solve this trade‐off between search time and search efficiency, we propose an efficient disjunctive search scheme using non‐positional inverted index. To the best of our knowledge, there is no searchable encryption scheme based on the non‐positional inverted index in the literature. Thus, we first propose a basic scheme based on a non‐positional inverted index to search the desired keywords and highlight its inefficiency in terms of high search time required. To perform efficient searching, an extended search scheme is proposed using keyword binning, which reduces comparisons required and improves the search time. The extended search scheme has recall of 100% and precision 99.75%. The experimental analysis of the proposed scheme on real datasets proves that the proposed scheme is privacy‐preserving and efficient.