Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP

In this survey, we present state-of-the-art bitrate adaptation algorithms for HTTP adaptive streaming (HAS). As a key distinction from other streaming approaches, the bitrate adaptation algorithms in HAS are chiefly executed at each client, i.e., in a distributed manner. The objective of these algorithms is to ensure a high quality of experience (QoE) for viewers in the presence of bandwidth fluctuations due to factors like signal strength, network congestion, network reconvergence events, etc. While such fluctuations are common in public Internet, they can also occur in home networksor even managed networks where there is often admission control and QoS tools. Bitrate adaptation algorithms may take factors like bandwidth estimations, playback buffer fullness, device features, viewer preferences, and content features into account, albeit with different weights. Since the viewer’s QoE needs to be determined in real-time during playback, objective metrics are generally used including number of buffer stalls, duration of startup delay, frequency and amount of quality oscillations, and video instability. By design, the standards for HAS do not mandate any particular adaptation algorithm, leaving it to system builders to innovate and implement their own method. This survey provides an overview of the different methods proposed over the last several years.
Mobility-Aware Hierarchical Clustering in Mobile Wireless Sensor Networks

Wireless sensor networks (WSNs) are one of the chief enabling technologies for the Internet of Things. These networks are severely resource-constrained which calls for designing energy-efficient and effective routing techniques. The hierarchical- or clustering-based routing approaches have shown to improve both energy-efficiency and scalability in WSNs. However, when clustering is implemented in mobile WSNs (MWSNs), the mobility of sensor nodes results in high data loss due to possible dis-association of nodes with their cluster heads which negatively affects the data rates and energy consumption. In order to mitigate the impact of node mobility on clustering, we propose two mobility-aware hierarchical clustering algorithms for MWSN based on three-layer clustering hierarchy: mobility-aware centralized clustering algorithm (MCCA) and mobility-aware hybrid clustering algorithm (MHCA). The MCCA algorithm employs centralized gridding at both layers of clustering hierarchy, and the MHCA algorithm employs centralized gridding at the upper layer and distributed clustering at the lower layer. The simulation results show that our proposed algorithms improve network lifetime, reduce energy consumption, stabilize cluster formation, and enhance data rates in mobile sensor networks. We also observe that the centralized clustering approach is superior to the hybrid clustering approach.
ACTL: Adaptive Codebook Transfer Learning for Cross-Domain Recommendation

Collaborative filtering usually suffers from limited performance due to the data sparsity problem. Transfer learning presents an unprecedented opportunity to alleviate this issue through transfer useful knowledge from an auxiliary domain to a target domain. Cluster-level rating patterns transformation models have been widely used due to the loose restriction which does not assume the source overlaps users and items with the target. However, previous researches have never investigated the relationship between the codebook scale in transfer learning and the prediction accuracy in the target domain. Moreover, all existing rating patterns sharing models fix the codebook scale without considering the data features of the source domain. In this paper, we propose a novel model, namely ACTL, to efficiently and automatically discover the appropriate codebook scale, which balances both the computational cost and prediction accuracy and best matches the size and features of the source domain for the cross-domain recommendation. The extensive experiments on real-world datasets demonstrate that our algorithms get knowledge gain from the large source domain and clearly and solidly outperform the state-of-the-art fixed scale codebook transfer learning methods.
Voluntary Vaccination through Perceiving Epidemic Severity in Social Networks

The severity of an epidemic has a significant impact on individual vaccinating decisions under voluntary vaccination. During the epidemic of a vaccine-preventable disease, individuals in a social network can perceive the infection risks based on global information announced by public health authorities, or local information obtained from their social neighbors. After that, they can rationally decide whether or not to take the vaccine through weighing the relative cost of vaccination and infection (i.e., relative vaccine cost). In this case, both social network structure and individuals’ risk perception strategies will affect the final vaccine coverage. In this paper, we focus on the problem of how individuals’ perceptions on epidemic severity affect their vaccinating behaviors in the face of flu-like seasonal diseases in social networks, and vice versa. Specifically, we first present three types of static decision-making mechanisms, each of which simulates human vaccinating behaviors based on different local/global information. On this basis, we further present a reinforcement-learning-based mechanism, where individuals can use their historical vaccination experiences to determine what information is more suitable to estimate the severity of the epidemic. Finally, we carry out simulations on three types of social networks to investigate the effects of network structure, source of information, relative vaccine cost, and individual social connections on the final vaccine coverage and epidemic size. The results and findings can provide a new insight for designing incentive-based vaccination policies and intervention strategies for flu-like seasonal diseases.
Hybrid human resources localization and tracking system

Organizational success and productivity depend on the effective and efficient utilization of Human Resources (HR). In view of the importance of HR, organizations put high premium on their safety, wellbeing and adequate monitoring. Several researchers have come up with solutions for monitoring HR movements but the major challenge has been the inability of a single technique to adequately and comprehensively monitor and provide accurate positioning data due to the changing environments of the workplace. This paper presents the implementation of a hybrid HR monitoring system using Global Positioning System (GPS), Radio Frequency Identification (RFID), cameras and sensors. The model is implemented using Python programming language as frontend and MySQL database management system as backend. Case study of the monitoring of selected staff of Information and Communication Technology Application Centre (ICTAC) of Adekunle Ajasin University, Akungba-Akoko, Nigeria was used to test the adequacy and practical functions of the model. Obtained data on positioning accuracy, signal sensitivity, cost of deployment and coverage area formed the basis for evaluation and comparative analysis.
Topological Influence-Aware Recommendation on Social Networks

Users in online networks exert different influence during the process of information propagation, and the heterogeneous influence may contribute to personalized recommendations. In this paper, we analyse the topology of social networks to investigate users’ influence strength on their neighbours. We also exploit the user-item rating matrix to find the importance of users’ ratings and determine their influence on entire social networks. Based on the local influence between users and global influence over the whole network, we propose a recommendation method with indirect interactions that makes adequate use of users’ relationships on social networks and users’ rating data. The two kinds of influence are incorporated into a matrix factorization framework. We also consider indirect interactions between users who do not have direct links with each other. Experimental results on two real-world datasets demonstrate that our proposed framework performs better than other state-of-the-art methods for all users and cold-start users. Compared with node degrees, betweenness, and clustering coefficients, coreness constitutes the best topological descriptor to identify users’ local influence, and recommendations with the measure of coreness outperform other descriptors of user influence.
On the Design and Implementation of an IoT based Architecture for Reading Ultra High Frequency Tags

Internet of Things comprises an increasing number of interconnected smart devices, where communication happens anytime, anywhere, reducing hardware costs and the complexity of the architectures. Reading Radio Frequency Identification tags using ultra high frequency is a high-cost activity because of its infrastructure and the radio frequency identification tag reading device for these frequencies. This work proposes an architecture that enables the cost reduction of Radio Frequency Identification tag reading equipment operating ultra high frequency in an infrastructure using cloud computing and microservices. The use of cloud computing and microservices was necessary due to scalability and the management of large volumes of data that can be generated by reading Radio Frequency Identification tags using ultra high frequency and the complexity of the architecture related to the theme of this work. The proposed architecture was applied in a real case study to verify its adherence and compliance. The proposed architecture can be used in any system that presents similar characteristics to the one proposed in this work. In scenarios where reading distance is a fundamental requirement, it is necessary to include an external antenna for better results. Other practical experiments will be carried out to evaluate the use of the proposed architecture in other contexts related to the use of Internet of Things and reading of Radio Frequency Identification tags.
Policy Modeling and Applications: State-of-the-Art and Perspectives

The range of application of methodologies of complexity science, interdisciplinary by nature, has spread even more broadly across disciplines after the dawn of this century. Specifically, applications to public policy and corporate strategies have proliferated in tandem. This paper reviews the most used complex systems methodologies with an emphasis on public policy. We briefly present examples, pros, and cons of agent-based modeling, network models, dynamical systems, data mining, and evolutionary game theory. Further, we illustrate some specific experiences of large applied projects in macroeconomics, urban systems, and infrastructure planning. We argue that agent-based modeling has established itself as a strong tool within scientific realm. However, adoption by policy-makers is still scarce. Considering the huge amount of exemplary, successful applications of complexity science across the most varied disciplines, we believe policy is ready to become an actual field of detailed and useful applications.
Face recognition performance using linear discriminant analysis and deep neural networks

The face recognition applications deal with large amounts of images and remain difficult to accomplish due to when displayed with images taken in unlimited conditions. Linear discriminant analysis (LDA) is a supervised method that uses training samples to obtain the projection matrix for feature extraction, while deep neural networks are trainable for supervised and unsupervised tasks. In this paper, we present our results of experiments done with linear discriminant analysis (LDA) and deep neural networks (DNN) for face recognition, while their efficiency and performance are tested on labelled faces in the wild (LFW) dataset. We used two methods of DNN, k-nearest neighbours algorithm (k-NN) and support vector machine (SVM). Experimental results show that the DNN method achieves better recognition accuracy and recognition time is much faster than the LDA method in large-scale datasets. Deep learning methods have shown high accuracy even for images coming out of the dataset.
Isabelle/UTP: Mechanised Theory Engineering for Unifying Theories of Programming

Isabelle/UTP is a mechanised theory engineering toolkit based on Hoare and He’s Unifying Theories of Programming (UTP). UTP enables the creation of denotational, algebraic, and operational semantics for different programming languages using an alphabetised relational calculus. We provide a semantic embedding of the alphabetised relational calculus in Isabelle/HOL, including new type definitions, relational constructors, automated proof tactics, and accompanying algebraic laws. Isabelle/UTP can be used to both capture laws of programming for different languages, and put these fundamental theorems to work in the creation of associated verification tools, using calculi like Hoare logics. This document describes the relational core of the UTP in Isabelle/HOL.
Optimised halftoning and inverse halftoning of dermoscopic images for supporting teledermoscopy system

Telemedicine systems require reliable, small size, and secure transmission. In teledermoscopy, the transmission of the traditional large size dermoscopic images leads to heavy consumption of storage space and bandwidth congestion, which impairs the efficient use of the transmission channel bandwidth. Furthermore, halftoning and inverse halftoning have a great practical impact in the reduction of the images' size. For teledermoscopy, the present work optimises the error filter during the error diffusion process to generate the halftone dermoscopic images with small size. The proposed inverse halftone optimises two parameters during the integration between the regularised inverse filter and the DLPA-ICI (directional local polynomial approximation and the intersection of confidence intervals) estimator's kernel. The optimised kernel parameter determines the best window size of the kernel in the restored image estimation. The experimental results included comparative studies in terms of several image quality metrics and the average size reduction in percentage when using the genetic algorithm for optimisation with Jarvis error diffusion for halftone images generation. The achieved average size reduction is 92.15% using the proposed method compared to the average dermoscopic images' size in the test set on 900 images showing the superiority of the proposed method.
Nonlinear Improved Concise Backstepping Control of Course Keeping for Ships

The backstepping control is a kind of nonlinear controller design algorithm. To simplify the nonlinear controller design process, decrease the number of undetermined parameters, improve the robustness, and reduce the energy consumption of the course-keeping controller for ships, an improved concise design method is proposed by introducing an arctan nonlinear function passed through by course error signal under the backstepping design framework, while the design process of the controller is simplified to only one step. The simulation results indicate that, compared with the backstepping-based controller, the maximum response performance (quantified by the mean absolute error) under the proposed controller increases by 57.8%, the maximum energy cost performance (quantified by the mean integral absolute) reduces by 28.6% and the maximum smoothness performance (quantified by the mean total variation) reduces by 44.3% with the strong ability of disturbances rejection. The algorithm given in the note has advantages of the simple design process, strong robustness, and low energy consumption.
Adaptive Fuzzy Super-Twisting Sliding Mode Control for Microgyroscope

This paper proposes a novel adaptive fuzzy super-twisting sliding mode control scheme for microgyroscopes with unknown model uncertainties and external disturbances. Firstly, an adaptive algorithm is used to estimate the unknown parameters and angular velocity of microgyroscopes. Secondly, in order to improve the performance of the system and the superiority of the super-twisting algorithm, this paper utilizes the universal approximation characteristic of the fuzzy system to approach the gain of the super-twisting sliding mode controller and identify the gain of the controller online, realizing the adaptive adjustment of the controller parameters. Simulation results verify the superiority and the effectiveness of the proposed approach, compared with adaptive super-twisting sliding mode control without fuzzy approximation; the proposed method is more effective.
Off-Grid DOA Estimation for Wideband LFM Signals in FRFT Domain Using the Sensor Arrays

For the direction-of-arrival (DOA) estimators based on sparse signal representation, the feasibility and precision are greatly restricted by the inherent limitation of the predefined spatial discrete grids. In this paper, based on the high aggregation characteristic of wideband linear frequency modulation signals, we derive the modified fractional domain sparse model (MFDSM) in DOA estimation and propose a novel off-grid DOA estimation method via alternating descent iteration (OGDEADI). The simulation results show that our proposed MFDSM-OGDEADI method has enhanced estimation accuracy and better angular resolution in terms of signal-to-noise ratio, angle difference, grid size, and angle bias, and it also has the advantage of being less sensitive to the grid size.
Service-oriented platform for smart operation of dyeing and finishing industry

ABSTRACT Ensuring sustainability is a primary concern of the manufacturing industry. Not only does the enhancement of process and systematic efficiency secure sustainability through increased energy efficiency but it also improves the efficiency in terms of overall productivity. Dyeing and finishing industries consume massive amounts of energy and have large energy-related expenditures. The industry comprises small- and medium-sized enterprises, which have insufficient capital to pay for the energy-efficient equipment. This study proposes a service-oriented platform that can achieve process and systematic efficiency enhancement based on the industrial Internet of Things (IIoT). The platform reflects the requirements of dyeing and finishing shops in the industry and secures effective interoperability and services using a service-oriented architectural design. A smart operation enabled by the service-oriented platform to improve the efficiency of traditional processes is developed. The system architecture, P4R data model, and service composition are discussed to understand the proposed process. Results suggest that it is possible to provide dyeing and finishing shops with services for reducing energy costs and improving productivity. The proposed platform also provides high-level services that utilize analytical techniques and data from the IIoT, and it serves as a suitable solution for small- and medium-sized enterprises by reducing costs.
Inter-frame forgery detection based on differential energy of residue

Inter-frame forgery marks a central type of forgery in surveillance videos, and involves three aspects - frame duplication, insertion, and deletion - under temporal domain. However, this forgery type has received little attention from scholars. More efforts have been on detecting only a single aspect of inter-frame forgery. Furthermore, studies have confirmed that previous methods did not achieve high accuracy for all forgeries types with low computational loads at the same time. In this study, the proposed method establishes a framework that can simultaneously detect all aspects of inter-frame forgeries. During the decoding process, the authors extract residue data of each frame from a video stream. Then spatial and temporal energies are exploited to illustrate data flow, and abnormal points are determined to detect forged frames. Noise ratios of forged and original frames are estimated for differentiating insertion from duplication attacks. Experimental results indicate that the proposed method achieves higher accuracy and lower computational time for detecting inter-frame forgery.
Feature selection method based on statistics of compound words for arabic text classification

One of the main problems of text classification is the high dimensionality of the feature space. Feature selection methods are normally used to reduce the dimensionality of datasets to improve the performance of the classification, or to reduce the processing time, or both. To improve the performance of text classification, a feature selection algorithm is presented, based on terminology extracted from the statistics of compound words, to reduce the high dimensionality of the feature space. The proposed method is evaluated as a standalone method and in combination with other feature selection methods (two-stage method). The performance of the proposed algorithm is compared to the performance of six well-known feature selection methods including Information Gain, Chi-Square, Gini Index, Support Vector Machine-Based, Principal Components Analysis and Symmetric Uncertainty. A wide range of comparative experiments were conducted on three Arabic standard datasets and with three classification algorithms. The experimental results clearly show the superiority of the proposed method in both cases as a standalone or in a two-stage scenario. The results show that the proposed method behaves better than traditional approaches in terms of classification accuracy with a 6-10% gain in the macro-average, F1.
A constraint-based job recommender system integrating FoDRA

We present a framework for a constraint-based recommender system that matches available job positions with job seekers. Our framework utilises the four dimensions recommendation algorithm (FoDRA) in which a job attribute (e.g., age of candidate) can be modelled in four classes: exact value (E), a range with lower limit (L), a range with upper limit (U) and a range with both lower and upper limit (LU). FoDRA allows us to better formulate the job seeking and recruiting domain in a computational form. We describe both the system architecture in a high-level and the algorithm formulation of the job seeking and recruiting domain required by FoDRA. Our framework is validated through comparative experiments with real data obtained from the website of Kaggle.
Privacy Preservation in Big Data From the Communication Perspective—A Survey

The advancement of data communication technologies promotes widespread data collection and transmission in various application domains, thereby expanding big data significantly. Sensitive information about individuals, which is typically evident or hidden in data, is prone to various privacy attacks and serious risks of privacy disclosure. Corresponding approaches to data privacy preservation have been proposed to provide mechanisms for preserving data privacy while pubilishing useful information or mining valuable information from sanitized data. In this work, we present a comprehensive survey of privacy preservation in big data from the communication perspective. Specifically, we cover the fundamental privacy-preserving framework and privacy-preserving technologies, particularly differential privacy. We also survey the adaptations and variants of differential privacy for different emerging applications and the challenges to differential privacy. In addition, we provide future research directions about privacy preservation in communication field.