Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.
Democracy and economic growth

Many nations consider democracy to be an important social value. Nevertheless, does it mean that countries with more democracy are often wealthier? What are the relationships between economic growth and democracy? This research includes 167 countries to study the issue. We employ the data of the democracy index, corruption perception index, inflation, population, number of internet users, balance of trade, foreign direct investment, etc. We have also included sub-indices such as the electoral process and pluralism, functioning of government, political participation, culture, and civil liberties. An innovative part of the paper is how the corruption perception index has been included in our analysis. Besides, principal component analysis is applied to study the relationship between democracy and economic growth. We conclude that it takes democracy a very long time to affect the macro-economy. The fast pace of change in democracy even harms the macro-economy. If the economy reaches a well-developed stage, the economy will gradually transform into a democratic city automatically in the absence of any external pressure.
Blind Spectrum Sensing Approaches for Interweaved Cognitive Radio System: A Tutorial and Short Course

Spectrum sensing is one of the essential tasks to have a cognitive radio system, which will allow an unlicensed user, called secondary user, to utilize the spectrum while the licensed user, called primary user, is not occupying it. The spectrum sensing approaches can be classified as blind and knowledge aided approaches. This tutorial summarizes blind spectrum sensing (BSS) approaches that require no prior knowledge of the licensed user’s signal characteristics, specifically for an interweave cognitive radio network model. The tutorial provides a thorough background, major implementations, and limitations of the BSS approaches, which are energy detector approach, maximum to minimum eigenvalue approach, maximum eigenvalue approach, covariance absolute value approach, and covariance Frobenius norm approach. Moreover, the tutorial compares these approaches based on performance metrics and complexity requirements. Furthermore, for a higher interference protection, the combination of two different spectrum sensing approaches, namely two-stage detection technique is presented and discussed. Besides, the tutorial discusses the challenges and possible future research directions. The fundamental objective of this tutorial is to provide insightful views and design aspects of BSS approach to researchers. For this purpose, the tutorial includes pseudo codes and simulation examples to illustrate more about the practical aspects of the above-mentioned approaches.
Zero-Error Capacity of Duplication Channels

This paper is concerned with the problem of error-free communication over the i.i. d. duplication channel which acts on a transmitted sequence <inline-formula> <tex-math notation="LaTeX">$x_{1} \cdots x_{n}$ </tex-math></inline-formula> by inserting a random number of copies of each symbol <inline-formula> <tex-math notation="LaTeX">$x_{i}$ </tex-math></inline-formula> next to the original symbol. The random variables representing the numbers of inserted copies at each position <inline-formula> <tex-math notation="LaTeX">$i$ </tex-math></inline-formula> are independent and take values in <inline-formula> <tex-math notation="LaTeX">$\{0, 1, \ldots, r\}$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> is a fixed parameter. A more general model in which blocks of <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> consecutive symbols are being duplicated, and which is inspired by DNA-based data storage systems wherein the stored molecules are subject to tandem-duplication mutations, is also analyzed. A construction of optimal codes correcting <italic>all</italic> patterns of errors of this type is described, and the zero-error capacity of the duplication channel—the largest rate at which information can be transmitted through it in an error-free manner—is determined for each <inline-formula> <tex-math notation="LaTeX">$\ell $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula>.
Deep Learning Applied on Rened Opinion Review Datasets

Deep Learning has been successfully applied in hard to solve areas, such as image recognition and audioclassification. However, Deep Learning has not yet reached the same performance when employed in textual data,including Opinion Mining. In models that implement a deep architecture, Deep Learning is characterized by theautomatic feature selection step. The impact of previous data refinement in the pre-processing step before theapplication of Deep Learning is investigated to identify opinion polarity. This refinement includes the use of aclassical procedure of textual content and a popular feature selection technique. The results of the experimentsovercome the results of the current literature with the Deep Belief Network application in opinion classification.In addition to overcoming the results, their presentation is broader than the related works, considering the changeof parameter variables. We prove that combining feature selection with a basic preprocessing step, aiming toincrease data quality, might achieve promising results with Deep Belief Network implementation.
Energy Efficiency Maximization Design for Full-Duplex Cooperative NOMA Systems With SWIPT

This paper investigates non-orthogonal multiple access (NOMA) system, where the near NOMA user is able to communicate with a base station (BS) directly while the far NOMA user needs to resort to a full-duplex (FD) relay. Wireless power transfer is applied to our system where the FD relay integrated with power splitting architecture can be powered wirelessly by the ambient radio signals simultaneously. Moreover, we assume that self-interference cancellation at the relay and inter-user interference cancellation at the near NOMA user are imperfect. For the purpose of maximizing the energy efficiency of the NOMA system, the power splitting ratio and transmit beamforming vectors are researched for the BS and relay with the guarantee of QoS requirements for two users. The formulated problem appears to be nonlinear fractional programming, which is highly nonconvex and the global optimality is hard to obtain. By introducing Charnes–Cooper’s transformation and inner approximation method, we develop an iterative algorithm to gain the near optimal solution. The simulation results show that the proposed joint optimizing algorithm outperforms the existing scheme in terms of energy efficiency as well as the effect of SI cancellation on performance gain.
An Adaptive Optimization Algorithm Based on Hybrid Power and Multidimensional Update Strategy

Recently, the adaptive learning rate optimization algorithm has shown excellent performances in the field of deep learning. However, the exponential moving average method can lead to convergence problem in some cases, such as it only converges to the sub-optimal minimum. Although AMSGrad algorithm provides solutions for convergence problems, the actual performance is close to or even weaker than that of Adam. In this paper, a new updating rule is proposed based on mixed high power historical and current squared gradients to construct a targeted first-order optimization algorithm for the adaptive learning rate. This algorithm not only overcomes the convergence problems encountered in most current optimization algorithms but also has a quick convergence. It outperforms the state-of-the-art algorithms on various real-world datasets, i.e., the forecast root-mean-square error performance is improved by about 20% on average than that of Adam and AMSGrad algorithms in time series prediction tasks.
Using Unsupervised Deep Learning Technique for Monocular Visual Odometry

Deep learning technique-based visual odometry systems have recently shown promising results compared to feature matching-based methods. However, deep learning-based systems still require the ground truth poses for training and the additional knowledge to obtain absolute scale from monocular images for reconstruction. To address these issues, this paper presents a novel visual odometry system based on a recurrent convolutional neural network. The system employs an unsupervised end-to-end training approach. The depth information of scenes is used alongside monocular images to train the network in order to inject scale. Poses are inferred only from monocular images, thus making the proposed visual odometry system a monocular one. The experiments are conducted and the results show that the proposed method performs better than other monocular visual odometry systems. This paper has made two main contributions: 1) the creation of the unsupervised training framework in which the camera ground truth poses are only deployed for system performance evaluation rather than for training and 2) the absolute scale could be recovered without the post-processing of poses.
E-assessment with multiple choice questions: a qualitative study of teachers' opinions and experience regarding the new assessment strategy

An e-assessment strategy, consisting of multiple choice questions (MCQ) within a Moodle learning management system (LMS) was implemented in a higher education institution to assist teachers with the assessment process. Considering that the new procedure induced major changes to the existing assessment, affecting students, teachers and staff, this paper aims to analyse teachers' perceptions through a qualitative study, applying a focus group constituted by senior lecturers who used MCQ tests in their courses. Resulting in the identification of advantages and disadvantages, eliciting improvements and exploring whether MCQ develops and evaluates the same knowledge and skills compared to existing solutions. In sum, the study's main result was the confirmation that e-assessment with MCQ remains reliable in any degree program course, although preferably when combined with another kind of assessment, such as a problem-based group project, which is effective in developing competencies and skills that MCQ are not.
An End-to-End Load Balancer Based on Deep Learning for Vehicular Network Traffic Control

The infrastructure to vehicle (I2V) communication boosts a large number of prevailing vehicular services, which can provide vehicles with external information, storage, and computing power located at both mobile edge server (MES) and remote cloud. However, vehicle distribution is imbalanced due to the spatial inhomogeneity and temporal dynamics. As a consequence, the communication load for MES is imbalanced and vehicles may suffer from poor I2V communications where the MES is overloaded. In this paper, we propose a novel proactively load balancing approach that enables efficient cooperation among MESs, which is referred to as end-to-end load balancer (E2LB). E2LB schedules the cached data among MESs based on the predicted road traffic situation. First, a convolutional neural network (CNN) is applied to efficiently learn the spatio-temporal correlation in order to predict the road traffic situation. Then, we formulate the load balancing problem as a nonlinear programming (NLP) problem and a novel framework based on CNN is adopted to approximate the NLP optimization. Finally, we connect the above neural networks into an end-to-end neural network to jointly optimize the performance, where the input is the historical traffic situation while the output is the balanced scheduling solution. E2LB can guarantee the real-time scheduling, since the calling of a well-trained neural network only requires a small number of simple operations. Experiments on the trajectories of taxis and buses in Beijing demonstrate the efficiency and effectiveness of E2LB.
Exploring Mobile Terminal Continuance Usage from Customer Value Perspective

With the large-scale growth of mobile terminal users, exploring their continuance use behavior attracts wide attention. However, there is insufficient research on user psychological states in different hierarchy models towards continuance use of a mobile terminal. This study advanced a mediation model of continuance use of a mobile terminal with a moderator based on expectation confirmation theory from the perspective of customer value dimension. Data were collected in China, and 311 samples were used to examine the hypotheses provided from the research model with the partial least squares structural equation modeling method. The empirical results indicate that perceived usefulness positively influences continuance use of a mobile terminal, and perceived usefulness positively influences satisfaction and in turn impacts on continuance use of a mobile terminal. Specially, there is a significant mediating effect of attitude loyalty between satisfaction and continuance use of a mobile terminal. Moreover, the results also verify that mobility negatively moderates the relationship between attitude loyalty and continuance use of a mobile terminal. Our findings suggest that user psychological states are in different levels to reflect mobile terminal user value during the process of continuance use of a mobile terminal. Moreover, our findings indicate the boundary condition in continuance use of a mobile terminal.
Study on drivers of online shopping and significance of cash-on-delivery mode of payment on behavioural intention

The purpose of this paper is to examine different drivers of online shopping and their impact on behavioural intention. The study also examines how cash-on-delivery mode of payment acts as mediator to improve online shopping intention. Based on UTAUT2 (Venkatesh et al., 2012), the drivers of online shopping were identified. Security and privacy and ease of ordering were also added to develop the construct of online shopping drivers. The mediating effect of cash-on-delivery mode of payment on behavioural intention has been empirically validated by structural equation modelling using a sample of 500 online shoppers. Results reveal that performance expectancy, effort expectancy, social influence, hedonic motivation, price value, security and privacy and ease of ordering are the drivers of online shopping but price value and facilitating conditions do not have significant impact on behavioural intention. Further, cash-on-delivery mode of payment emerged as a key construct to improve online shopping intention.
Logical Foundations of Linked Data Anonymisation

The widespread adoption of the Linked Data paradigm has been driven by the increasing demand for information exchange between organisations, as well as by regulations in domains such as health care and governance that require certain data to be published. In this setting, sensitive information is at high risk of disclosure since published data can be often seamlessly linkedwith arbitrary external data sources.In this paper we lay the logical foundations of anonymisation in the context of Linked Data. We consider anonymisations of RDF graphs (and, more generally, relational datasets with labelled nulls) and define notions of policy-compliant and linkage-safe anonymisations. Policy compliance ensures that an anonymised dataset does not reveal any sensitive information as specified by a policy query. Linkage safety ensures that an anonymised dataset remains compliant even if it is linked to (possibly unknown) external datasets available on the Web, thus providing provable protection guarantees against data linkage attacks. We establish the computational complexity of the underpinning decision problems both under the open-world semantics inherent to RDF and under the assumption that an attacker has complete, closed-world knowledge over some parts of the original data.
Investigating the employee work performance in task interdependence and ESM environment

This study intends to investigate how enterprise social media usage (ESM) is associated with work performance via task interdependence. Most scholars focus on the impact of ESM usage on employee's work performance; however, researchers are unclear about the advantages of ESM usage on work performance through task structure. This research offers and tests a new comprehensive model that links the ESM usage with task interdependence. Data were gathered from 170 representatives who utilised ESM applications for daily work routines in the workplace. The moderating role of work cooperation and IT competency were also examined. The present study validates most of the proposed hypotheses and moderating effects. Findings show that work cooperation shows significant moderating effect. However, the moderating effect of IT competency shows insignificant effect. This research study also contributes to the existing literature by suggesting managers the benefits of ESM usage and illuminating its consequences on employee work performance.
The Surprising Power of Constant Depth Algebraic Proofs

A major open problem in proof complexity is to prove superpolynomial lower bounds for AC0[p]-Frege proofs. This system is the analog of AC0 [p], the class of bounded depth circuits with prime modular counting gates. Despite strong lower bounds for this class dating back thirty years ([28, 30]), there are no significant lower bounds for AC0 [p]-Frege. Significant and extensive degree lower bounds have been obtained for a variety of subsystems of AC0[p]-Frege, including Nullstellensatz ([3]), Polynomial Calculus ([9]), and SOS ([14]). However to date there has been no progress on AC0 [p]-Frege lower bounds. In this paper we study constant-depth extensions of the Polynomial Calculus [13]. We show that these extensions are much more powerful than was previously known. Our main result is that small depth (≤ 43) Polynomial Calculus (over a sufficiently large field) can polynomially effectively simulate all of the well-studied semialgebraic proof systems: Cutting Planes, Sherali-Adams, Sum-of-Squares (SOS), and Positivstellensatz Calculus (Dynamic SOS). Additionally, they can also quasi-polynomially effectively simulate AC0[q]-Frege for any prime q independent of the characteristic of the underlying field. They can also effectively simulate TC0-Frege if the depth is allowed to grow proportionally. Thus, proving strong lower bounds for constant-depth extensions of Polynomial Calculus would not only give lower bounds for AC0 [p]-Frege, but also for systems as strong as TC0-Frege.
Multi-stage age estimation using two level fusions of handcrafted and learned features on facial images

Age estimation from facial images is an important application of biometrics. In contrast to other facial variations like occlusions, illumination, misalignment and facial expressions, ageing variation is affected by human genes, environment, lifestyle and health which make age estimation a challenging task. In this study, the authors propose a new age estimation system which exploits multi-stage features from a generic feature extractor, a trained convolutional neural network (CNN), and precisely combined these features with a selection of age-related handcrafted features. This method utilises a decision-level fusion of estimated ages by two different approaches; the first one uses feature-level fusion of different handcrafted local feature descriptors for wrinkle, skin and facial component, while the second one uses score-level fusion of different feature layers of a CNN for its age estimation. Experiments on the publicly available MORPH-Album-2 and FG-NET databases prove the effectiveness of the novel method. Moreover, an additional experimental study on AgeDB database demonstrates that the proposed method is comparable with the best state-of-the-art system for age estimation using in-the-wild age databases.
Factors Influencing User Acceptance of Web-Based Decision Support Systems

Web-Based Decision Support Systems (WDSS) have emerged in the past decades. However, with their flourishing, many deficiencies have also appeared. These have led to a low acceptance rate of WDSS. Thus, knowing what these deficiencies or influencing factors has become very important to those firms intending to implement their own WDSS. This study constructed an empirical study to investigate a famous portal Web site in Taiwan. One hundred and seventy effective responses were collected. The results indicate that all hypotheses, except perceived risk and response time, are supported. Attitude, the subjective norm and perceived behavioral control are determinants to explain a user's intention to use WDSS. Furthermore, compatibility, trust, perceived ease of use and perceived usefulness have a significant effect on attitude, interpersonal and external influence have a notable impact on the subjective norm; and self efficacy and facilitating conditions also have a significant consequence on perceived behavioral control.
Impact of Motivating Language on Team Creative Performance

An abundance of previous theories and empirical studies have verified that leadership styles indeed can influence members' creativity and effectiveness in both traditional team settings and in a virtual context. Moreover, past research has also indicated that leaders can build relationships among team members and increase mutual trust due to the face to face interaction made possible in a traditional office environment. However, there is little empirical work that articulates how leaders behave and motivate their members in a virtual context. This study employed an experimental design with 50 undergraduate juniors who acted as the team members and 23 Executive MBA students who acted as the team leaders. The main goal was to study the impact of leaders' motivating language (i.e., empathetic, direction-giving, and mixed usage) on team members' creative performance. Key findings indicate that there are slightly significant differences between three kinds of motivating language approaches. The study shows that leaders who give virtual team members feedback via written communication that is both directional and empathetic are perceived as more effective and lead to better performance.