Miracle-dz/UniFAR-FTUs huggingface dataset:
Computing Offloading Over mmWave for Mobile VR: Make 360 Video Streaming Alive

Compared with previous video streaming technologies, 360 video streaming is an emerging technology in commercial platforms that promise greater immersive video service. The use of 360 video streaming requires larger bandwidth and rapid responsiveness to users. In addition, mobile virtual-reality (VR) devices locally process video decoding, post-processing, and rendering. However, its performance is insufficient to stream high-resolution videos such as 4k–8k. Therefore, we propose an adaptive computing offloading scheme using millimeter wave (mmWave) communication. This offloading scheme helps mobile device sharing video decoding tasks to a powerful GPU-based PC. It allows the mobile VR device enhancing the capability of playing high-resolution videos. The mmWave 802.11ad wireless technology promises the use of high-bandwidth wireless communication to improve the capacity of multimedia systems. However, the current studies on video transmission over 802.11ad mmWave are specifically focused on supporting the outdoor environment. Furthermore, synchronization is an essential issue for ensuring the quality of service (QoS) of 360 video transmission. Thus, the proposed scheme concentrates on two parts: 1) 360 video streaming in an indoor environment, and offloading mechanism using mmWave 802.11ad 60-GHz wireless communication to offload video coding tasks and 2) adaptive video synchronization mechanisms, and high-efficiency video coding (HEVC) using scalable HEVC extension for 360 videos with equirectangular projection. The experiments prove that the proposed scheme provides high performance 360 video streaming in QoS-sensitive video streaming applications.
Stabilization of MIMO systems over additive correlated noise channels subject to multiple SNR-constraints

This paper studies the mean square stabilization of MIMO discrete-time linear time-invariant systems over a MIMO additive correlated channel. We assume that such channel consists of multiple correlated SISO channels subject to independent input signal-to-noise ratio (SNR) constraints. We derive explicit conditions for which mean square stabilization can be achieved under such constraints for unstable minimum phase plants, and characterize the controller that achieves such SNR. We also present the set of admissible SNR constraints for mean square stability for a particular set of plants. Our results show that noise correlation can reduce the SNR requirements for stability compared to independent additive white noise channels. In addition, a numerical simulation is provided to illustrate the theoretical results.
Air Transport Services a Monopoly in Air Navigation Services

Air traffic over the Czech Republic has continued to grow in recent years. The Air Navigation Services of the Czech Republic, as the company responsible for the safety of air navigation in the Czech Republic, plays in it a vital role. The purpose of this paper is to elaborate the economic performance of Air Navigation Services of the Czech Republic (ANS CR) in the context of regulation in this sector to evaluate its basic parameters of performance in the context of its status as a monopoly, and to assess its competitiveness in terms of the objectives of SES legislation. With the exception of advertising and product differentiation, the mission of the company gives it the character of a monopoly in the air navigation services market. The elaboration of this paper is based in particular on the methods used in describing the mission and activities of the company, as well as on the method of comparative analysis in evaluating the development of the performance indicators of the company. Economic globalization has allowed the company to improve its position considerably among other European partners. This has been mainly due to the growth of tourism, which can be traced to the emergence of low-cost airlines and a drop in fares in general. The deployment of wide-body passenger airliners has been another force in the growth of the economic performance of ANS CR. These factors suggest further, positive developments for the company in the future.
The Role of Self-Control and Motivation on Exhaustion in Youth Athletes: A Longitudinal Perspective

The depletion of self-control competencies has been explained by an external shift in motivation, and recent research has emphasized that controlled types of motivation and self-control competencies are positively associated with exhaustion in youth athletes. Using the self-determination theory (SDT) and self-control theories, this study examined associations between athletes' motivation, self-control competencies, and exhaustion experiences throughout a competitive season. A total of 321 winter sport youth athletes (173 males, 98 females, and 50 unknown gender; aged 16 to 20 years, M = 17.98, SD = 0.89) participated in this 10-week longitudinal study, including three time points. Using Bayesian structural equation modeling, associations between athletes' reported level of motivation regulations, self-control, and exhaustion throughout their competitive season were examined in two mediation models. Constructs were associated in a conceptual and consistent manner. Simple mediation models showed credible indirect and direct effects of motivation on exhaustion via self-control within amotivation, and intrinsic, integrated, identified, and external regulation analyses. These credible effects were not replicated in the focused mediation model, when controlling for self-control and exhaustion autoregressive effects. However, direction of effects in both models was consistent and congruent. Findings consistently supported the interplay between motivation and exhaustion via self-control in youth athletes over an important competition period of the year. Autonomous and controlled motivation interacted with self-control and, respectively, predicted perceived exhaustion negatively and positively. Thus, autonomous self-control motives are important in preventing negative sport participation development over time. However, simple and focused mediation models showed different results, suggesting a necessity for accurate considerations of analytical methods chosen to investigate longitudinal mediation. Specifically, future studies need to carefully consider the time interval between measurement time points when investigating changes in dynamic psychological constructs, and include autoregressive longitudinal effects in order to predict change in levels of the outcome over time.
Incremental Stability of Hybrid Dynamical Systems

The analysis of incremental stability typically involves measuring the distance between any two solutions of a given dynamical system at the same time instant, which is problematic when studying hybrid dynamical systems. Indeed, hybrid systems generate solutions defined with respect to hybrid time instances (that consists of both the continuous time elapsed and the discrete time, which is the number of jumps experienced so far), and two solutions of the same hybrid system may not be defined at the same hybrid time instant. To overcome this issue, we present novel definitions of incremental stability for hybrid systems based on graphical closeness of solutions. As we will show, defining incremental asymptotic stability with respect to the hybrid time yields a restrictive notion, such that we also investigate incremental asymptotic stability notions with respect to the continuous time only or the discrete time only, respectively. In this manner, two (effectively dual) incremental stability notions are attained, called jump- and flow incremental asymptotic stability. To present Lyapunov conditions for these two notions, in both cases, we resort to an extended hybrid system and we prove that the stability of a well-defined set for this extended system implies incremental stability of the original system. We can then use available Lyapunov conditions to infer the set stability of the extended system. Various examples are provided throughout this paper, including an event-triggered control application and a bouncing ball system with Zeno behavior, that illustrate incremental stability with respect to continuous time or discrete time, respectively.
A Novel Robust Fuzzy Integral Sliding Mode Control for Nonlinear Semi-Markovian Jump T–S Fuzzy Systems

This paper addresses the issue of robust fuzzy sliding mode control for continuous-time nonlinear Takagi–Sugeno fuzzy systems with semi-Markovian switching. The focus is on designing a novel fuzzy integral sliding surface without assuming that the input matrices are the same with full column rank and then developing a fuzzy sliding-mode controller for stochastic stability purpose. Based on Lyapunov theory, a set of newly developed linear matrix inequality conditions are established for stochastic stability of the sliding-mode dynamics with generally uncertain transition rates, and then extended to where the input matrix is plant-rule-independent, as discussed in most existing literatures. Furthermore, finite-time reachability of the sliding surface is also guaranteed by the proposed fuzzy sliding-mode control laws. A practical example is provided to demonstrate the effectiveness of the established method numerically.
Who Will Like the Post? A Case Study of Predicting Likers on Flickr

Reacting to a published post on a social media is one of the main activities of users which can happen in different forms comprising to like the post, leave a comment or reshare it. Finding a way to predict the size of users future interactions and more interestingly identifying the users who are going to react to a post are the two important research topics which benefit different domains from efficient advertising campaign to enhanced content delivery systems. In this paper, we aim to predict the users who are going to react to a newly published post in future. Toward this aim, we implement a novel approach based on Point-wise Mutual Information (PMI) which derives users latent similarities from their interactions log and exploits them to predict future interacting users. The proposed method is evaluated using a large dataset of Flickr including 2.3M users and 11.2M published photos. The empirical findings support the idea of employing interactions log to detect future likers of posts by achieving noticeable prediction results for the tested dataset. Moreover, the analysis of the prediction task implies that likers prediction for the photos of publishers with a high number of followers and engagements is more accurate than the other publishers photos.
A Measurement Set-Up Method to Evaluate Motor Winding Quality and Winding Insulation by Exploring Discharge Characteristics

The development of new semiconductor materials such as SiC and GaN based designs introduces shorter rise times as well as increased carrier frequencies in power electronic applications. One very common reason for motor winding failures is due to the presence of partial discharges (PDs). This paper presents a test method to detect and evaluate the presence of PDs employing wave shapes similar or close to what is used in applications. The waveform is generated by applying a novel three-level inverter circuit. The set-up and detection method can for example be used to evaluate the influence of rise times, reflections and insulation conductivity on simple twisted pair test samples or complete windings. Initial measurement results from tests on twisted pair aimed to resemble a random wound motor are presented. In particular, the influence of conductivity, permittivity and rise time is explored by applying square shaped waveforms. The PDs appearing after the unipolar flanks are compared with full size bipolar voltage steps. When the zero voltage was reached the remining charges within and surrounding the defect produced a PD pattern (PD echo) resembling the charge amount within the winding. Tests showed a significant difference due to the choice of material. The introduced measurement approach maintains the advantages of using impulses to evaluate windings but complemented with charge decay measurement.
Italian Recommendations for Placental Transfusion Strategies

At delivery, if the cord is not clamped, blood continues to pass from the placenta to the newborn during the first minutes of life, allowing the transfer of 25–35 ml/kg of placental blood to the newborn, depending on gestational age, the timing of cord clamping, the position of the infant at birth, the onset of respiration, and administration of uterotonics to the mother. However, deriving benefits from delayed cord clamping (DCC) are not merely related to placental-to-fetal blood transfusion; establishing spontaneous ventilation before cutting the cord improves venous return to the right heart and pulmonary blood flow, protecting the newborn from the transient low cardiac output, and systemic arterial pressure fluctuations. Recent meta-analyses showed that delayed cord clamping reduces mortality and red blood cell transfusions in preterm newborns and increases iron stores in term newborns. Various authors suggested umbilical cord milking (UCM) as a safe alternative when delayed cord clamping is not feasible. Many scientific societies recommend waiting 30–60 s before clamping the cord for both term and preterm newborns not requiring resuscitation. To improve the uptake of placental transfusion strategies, in 2016 an Italian Task Force for the Management of Umbilical Cord Clamping drafted national recommendations for the management of cord clamping in term and preterm deliveries. The task force performed a detailed review of the literature using the GRADE methodological approach. The document analyzed all clinical scenarios that operators could deal with in the delivery room, including cord blood gas analysis during delayed cord clamping and time to cord clamping in the case of umbilical cord blood banking. The panel intended to promote a more physiological and individualized approach to cord clamping, specifically for the most preterm newborn. A feasible option to implement delayed cord clamping in very preterm deliveries is to move the neonatologist to the mother's bedside to assess the newborn's clinical condition at birth. This option could safely guarantee the first steps of stabilization before clamping the cord and allow DCC in the first 30 s of life, without delaying resuscitation. Contra-indications to placental transfusion strategies are clinical situations that may endanger mother ‘s health and those that may delay immediate newborn's resuscitation when required.
Generalization in anti-causal learning

The ability to learn and act in novel situations is still a prerogative of animate intelligence, as current machine learning methods mostly fail when moving beyond the standard i.i. d. setting. What is the reason for this discrepancy? Most machine learning tasks are anti-causal, i.e., we infer causes (labels) from effects (observations). Typically, in supervised learning we build systems that try to directly invert causal mechanisms. Instead, in this paper we argue that strong generalization capabilities crucially hinge on searching and validating meaningful hypotheses, requiring access to a causal model. In such a framework, we want to find a cause that leads to the observed effect. Anti-causal models are used to drive this search, but a causal model is required for validation. We investigate the fundamental differences between causal and anti-causal tasks, discuss implications for topics ranging from adversarial attacks to disentangling factors of variation, and provide extensive evidence from the literature to substantiate our view. We advocate for incorporating causal models in supervised learning to shift the paradigm from inference only, to search and validation.
Alternative Scoring Factors using Non-Financial Data for Credit Decisions in Agricultural Microfinance

Financial exclusion has a major socio-economic impact on the poor and unbanked. Financially excluded smallholder farmers face challenges accessing credit facilities to fund their farming activities because they lack the financial history data required to create credit scores for credit risk evaluations. Non-financial data sources such as mobile applications have been proposed for the development of credit scoring models. However, context-specific alternative scoring factors which are independent of financial history information, must be developed for credit decision systems that use nonfinancial data. This research proposes an approach to developing alternative scoring factors based on stakeholder's requirements. An example of implementation of the proposed method is given using data collected from farmers in rural Cambodia through surveys and a mobile application. Alternative scoring factors are developed based on stakeholder's requirements and collected data. Multiple logistic regression and support vector machine models are trained and tested on this data to evaluate the selected factors. Models are compared by area under the receiver operating characteristics curve values and accuracy. Additional considerations are made to determine the most suitable model in this context. This stakeholder requirements-based approach can be used to design credit decision systems using nonfinancial data for financially excluded persons and facilitate greater financial inclusion.
Show me your face and I will tell you your height, weight and body mass index

Body height, weight, as well as the associated and composite body mass index (BMI) are human attributes of pertinence due to their use in a number of applications including surveillance, re-identification, image retrieval systems, as well as healthcare. Previous work on automated estimation of height, weight and BMI has predominantly focused on 2D and 3D full-body images and videos. Little attention has been given to the use of face for estimating such traits. Motivated by the above, we here explore the possibility of estimating height, weight and BMI from single-shot facial images by proposing a regression method based on the 50-layers ResNet-architecture. In addition, we present a novel dataset consisting of 1026 subjects and show results, which suggest that facial images contain discriminatory information pertaining to height, weight and BMI, comparable to that of body-images and videos. Finally, we perform a gender-based analysis of the prediction of height, weight and BMI.
Global Context Encoding for Salient Objects Detection

Deep convolutional neural networks (CNNs) have gained their reputation for the success in various tasks in computer vision, including salient objects detection. However, it remains a challenge that the CNNs have repeated downsample operators and always create low-resolution predictions, which tend to loss details and finer structure of images. To detect and segment the salient objects well, it is also necessary to merge high-level semantic information and low-level fine details simultaneously. Thus, we propose a novel network structure with stage-wise refinement sub-structures. In addition, we exploit the essence of salient objects detection by encoding the global image context in a specifically designed module, which is applied to every stage of the refinement structure. So the coarse saliency map generated from the base CNN can be refined with low-level feature and global context information step-by-step. Experimental results have demonstrated that the proposed method outperforms the state-of-the-art approaches on four benchmark datasets.
Detecting DDoS Attack in Cloud Computing Using Local Outlier Factors

Now a days, Cloud computing has brought a unbelievable change in companies, organizations, firm and institutions etc. IT industries is advantage with low investment in infrastructure and maintenance with the growth of cloud computing. The Virtualization technique is examine as the big thing in cloud computing. Even though, cloud computing has more benefits; the disadvantage of the cloud computing environment is ensuring security. Security means, the Cloud Service Provider to ensure the basic integrity, availability, privacy, confidentiality, authentication and authorization in data storage, virtual machine security etc. In this paper, we presented a Local outlier factors mechanism, which may be helpful for the detection of Distributed Denial of Service attack in a cloud computing environment. As DDoS attack becomes strong with the passing of time, and then the attack may be reduced, if it is detected at first. So we fully focused on detecting DDoS attack to secure the cloud environment. In addition, our scheme is able to identify their possible sources, giving important clues for cloud computing administrators to spot the outliers. By using WEKA (Waikato Environment for Knowledge Analysis) we have analyzed our scheme with other clustering algorithm on the basis of higher detection rates and lower false alarm rate. DR-LOF would serve as a better DDoS detection tool, which helps to improve security framework in cloud computing.
Robo-Advisors and Financial Decision Inertia: How Choice Architecture Helps to Reduce Inertia in Financial Planning Tools

Robo-advisors are web applications for financial planning guiding users through a self-assessment process to provide and support portfolio generation. The aim of this study was a choice architecture approach to investigate the use of such robo-advisors to overcome biased financial decision-making, in particular on decision inertia in investment decisions. For that purpose, a robo-advisor was constructed and a simple market simulation was built to investigate the effectiveness of two digital nudges (defaults and warning messages) on financial decision inertia in a dual-choice paradigm. Furthermore, the study controlled for confounding variables like e.g. financial literacy or risk aversion. The result were shown to lead to a significant influence of both nudges on reduced decision inertia. Further findings suggests a trend in gender inequality for financial decision inertia. This indicates that there is a possibility of reducing financial decision inertia by robo-advisor design, and it suggests that financial planning tools can help users to overcome their financial decision biases. Besides the findings point towards further research in context of gender-sensitive IS design.
Microservices: A Mapping Study for Internet of Things Solutions

The Internet of Things (IoT) involves the connectivity of a number of different physical and virtual devices, allowing the development of new services and applications. These intelligent objects, along with their tasks, constitute domain-specific applications (vertical markets), while ubiquitous and analytical services form domain-independent services (horizontal markets). The development of these applications and services in these markets brings challenges such as deployment, scalability, integration, interoperability, mobility and performance. Recent researches indicates that Microservices have been adopted in several domains such as IoT. This paper provides an overview of the current state of the art (studies selected by May 2018) regarding the adoption of Microservices in the development of IoT applications by means of the mapping study methodology. In this context, we present the eighteen selected studies, the existing contributions and the future research perspectives.