Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks

The wireless mesh network (WMN) has proven to be a great choice for network communication technology. WMNs are composed of access points (APs) that are installed and communicate with each other through multi-hop wireless networks. One or more of these APs acts as a gateway (GW) to the internet. Hosts of WMNs are stationary or mobile. According to the structure of WMNs, some network features may be affected, such as the overall performance, channel interference, and AP connectivity. In this paper, we propose a new adaptive channel allocation algorithm for a multi-radio multi-channel wireless mesh network. The algorithm is aimed to minimize the number of channel reassignments while maximizing the performance under practical constraints. The algorithm defines a decision function for the channel reassignments. The decision function aims to minimize the traffic around the GW. Whenever the traffic changes in the wireless mesh network, the decision function decides which channel radio reassignment should be done. We demonstrated the effectiveness of our algorithm through extensive simulations using Network Simulator 2 (NS-2).
Building Fine-Grained Configurable ITRON Based RTOS

: As IoT (Internet of Things) is prevailing, the number of devices which have strict resource constraints is increasing. In developing such a system, RTOS (Real Time Operating System) helps to increase productivity. However, in the view of cost reduction, it is desirable that resources for RTOS be small and the execution time be short. In this paper, we propose a method to develop an application-speciﬁc system with RTOS. Methods of removing unnecessary code for the application from RTOS kernel are explained. In addition, we implemented a reconﬁgurable hardware RTOS on an FPGA and applied the method for removing unnecessary code from the hardware implementation. The evaluation results show that the proposed methods reduce hardware resources, RTOS kernel execution time, and the size of the software parts in each application.
q-Rung Orthopair Fuzzy Geometric Aggregation Operators Based on Generalized and Group-Generalized Parameters with Application to Water Loss Management

The notions of fuzzy set (FS) and intuitionistic fuzzy set (IFS) make a major contribution to dealing with practical situations in an indeterminate and imprecise framework, but there are some limitations. Pythagorean fuzzy set (PFS) is an extended form of the IFS, in which degree of truthness and degree of falsity meet the condition 0≤Θ˘2(x)+K2(x)≤1. Another extension of PFS is a q´-rung orthopair fuzzy set (q´-ROFS), in which truthness degree and falsity degree meet the condition 0≤Θ˘q´(x)+Kq´(x)≤1,(q´≥1), so they can characterize the scope of imprecise information in more comprehensive way. q´-ROFS theory is superior to FS, IFS, and PFS theory with distinguished characteristics. This study develops a few aggregation operators (AOs) for the fusion of q´-ROF information and introduces a new approach to decision-making based on the proposed operators. In the framework of this investigation, the idea of a generalized parameter is integrated into the q´-ROFS theory and different generalized q´-ROF geometric aggregation operators are presented. Subsequently, the AOs are extended to a “group-based generalized parameter”, with the perception of different specialists/decision makers. We developed q´-ROF geometric aggregation operator under generalized parameter and q´-ROF geometric aggregation operator under group-based generalized parameter. Increased water requirements, in parallel with water scarcity, force water utilities in developing countries to follow complex operating techniques for the distribution of the available amounts of water. Reducing water losses from water supply systems can help to bridge the gap between supply and demand. Finally, a decision-making approach based on the proposed operator is being built to solve the problems under the q´-ROF environment. An illustrative example related to water loss management has been given to show the validity of the developed method. Comparison analysis between the proposed and the existing operators have been performed in term of counter-intuitive cases for showing the liability and dominance of proposed techniques to the existing one is also considered.
Is Green Marketing a Label for Ecotourism? The Romanian Experience

The focus on sustainability represents the key to the tourism industry today. Green marketing was constantly on the agenda of local authorities and started to be a subject of interest for Academia too, but it was very little approached in the Eastern European countries, especially in the tourism studies. This article describes the presence of green marketing in Romanian ecotourism and its impact on tourist’s perception. Due to its natural landscapes and tourist attractions, Romanian ecotourism can take advantage of green marketing practices and promote tourism products based on the new preference of the tourists for sustainable activities. However, there is a lack of legislative framework regarding green labeling that can effect in the long term the destination brand. The main objective of the present article is to give an overview image of the online presence of the Romanian ecotourist accommodation units that applied for the Eco-label provided by the ECO-Romania Association. The study describes how these establishments use green marketing practices and if their guests perceive the green image too. The methodology of the study consists of a qualitative analysis of the primary data obtained by the authors from the websites of ECO Romania Association, TripAdvisor platform and accommodation units. The results show a high interest of the accommodation units from the ecotourism field for green marketing practices which is also perceived by tourists. The tendency of tourism stakeholders to apply for an accreditation system provided by non-governmental organization instead of choosing a European accreditation shows the impact of such a national initiative and emphasizes the need to develop a legal framework for green ecotourism practices.
Three Flavor Quasi-Dirac Neutrino Mixing, Oscillations and Neutrinoless Double Beta Decay

The extension of the Standard model by three right-handed neutrino fields exhibit appealing symmetry between left-handed and right-handed sectors, which is only violated by interactions. It can accommodate three flavor quasi-Dirac neutrino mixing scheme, which allows processes with violation of both lepton flavor and total lepton number symmetries. We propose a 6×6 unitary matrix for parameterizing the mixing among three flavors of quasi-Dirac neutrino. This mixing matrix is constructed by two 3×3 unitary matrices that diagonalizes the Dirac mass term in the Lagrangian. By only assuming the Standard Model V−A weak interaction, it is found that probabilities of neutrino oscillations among active flavor states and effective masses measured by single beta decay, by neutrinoless double-beta decay and by cosmology only depend on single 3×3 unitary matrix relevant to mixing of active neutrino flavors. Further, by considering 1σ and 3σ uncertainties in the measured oscillation probability of electron antineutrino from reactor, derivation of the constraint on the Majorana neutrino mass component is demonstrated. The consequence for effective Majorana neutrino mass governing the neutrinoless double-beta decay is discussed.
Chirped Laser Pulse Effect on a Quantum Linear Oscillator

We present a theoretical study of the excitation of a charged quantum linear oscillator by chirped laser pulse with the use of probability of the process throughout the pulse action. We focus on the case of the excitation of the oscillator from the ground state without relaxation. Calculations were made for an arbitrary value of the electric field strength by utilizing the exact expression for the excitation probability. The dependence of the excitation probability on the pulse parameters was analyzed both numerically and by using analytical formulas.
Joint Tx-Rx Beamforming and Power Allocation for 5G Millimeter-Wave Non-Orthogonal Multiple Access (MmWave-NOMA) Networks

In this paper, we investigate the combination of non-orthogonal multiple access and millimeter-Wave communications (mmWave-NOMA). A downlink cellular system is considered, where an analog phased array is equipped at both the base station and users. A joint Tx-Rx beamforming and power allocation problem is formulated to maximize the achievable sum rate (ASR) subject to a minimum rate constraint for each user. As the problem is non-convex, we propose a sub-optimal solution with three stages. In the first stage, the optimal power allocation with a closed form is obtained for an arbitrary fixed Tx-Rx beamforming. In the second stage, the optimal Rx beamforming with a closed form is designed for an arbitrary fixed Tx beamforming. In the third stage, the original problem is reduced to a Tx beamforming problem by using the previous results, and a boundary-compressed particle swarm optimization (BC-PSO) algorithm is proposed to obtain a sub-optimal solution. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results show that the proposed sub-optimal solution can achieve a near-upper-bound performance in terms of ASR, which is significantly improved compared with those of the state-of-the-art schemes and the conventional mmWave orthogonal multiple access (mmWave-OMA) system.
A Cost-Effective System for Aerial 3D Thermography of Buildings

Three-dimensional (3D) imaging and infrared (IR) thermography are powerful tools in many areas in engineering and sciences. Their joint use is of great interest in the buildings sector, allowing inspection and non-destructive testing of elements as well as an evaluation of the energy efficiency. When dealing with large and complex structures, as buildings (particularly historical) generally are, 3D thermography inspection is enhanced by Unmanned Aerial Vehicles (UAV—also known as drones). The aim of this paper is to propose a simple and cost-effective system for aerial 3D thermography of buildings. Special attention is thus payed to instrument and reconstruction software choice. After a very brief introduction to IR thermography for buildings and 3D thermography, the system is described. Some experimental results are given to validate the proposal.
Tracking of Deformable Objects Using Dynamically and Robustly Updating Pictorial Structures

The problem posed by complex, articulated or deformable objects has been at the focus of much tracking research for a considerable length of time. However, it remains a major challenge, fraught with numerous difficulties. The increased ubiquity of technology in all realms of our society has made the need for effective solutions all the more urgent. In this article, we describe a novel method which systematically addresses the aforementioned difficulties and in practice outperforms the state of the art. Global spatial flexibility and robustness to deformations are achieved by adopting a pictorial structure based geometric model, and localized appearance changes by a subspace based model of part appearance underlain by a gradient based representation. In addition to one-off learning of both the geometric constraints and part appearances, we introduce a continuing learning framework which implements information discounting i.e., the discarding of historical appearances in favour of the more recent ones. Moreover, as a means of ensuring robustness to transient occlusions (including self-occlusions), we propose a solution for detecting unlikely appearance changes which allows for unreliable data to be rejected. A comprehensive evaluation of the proposed method, the analysis and discussing of findings, and a comparison with several state-of-the-art methods demonstrates the major superiority of our algorithm.
Bivariate Power-Skew-Elliptical Distribution

In this article, we introduce a power-skew-elliptical (PSE) distribution in the bivariate setting. The new bivariate model arises in the context of conditionally specified distributions. The proposed bivariate model is an absolutely continuous distribution whose marginals are univariate PSE distributions. The special case of the bivariate power-skew-normal (BPSN) distribution is studied in details. General properties of the BPSN distribution are derived and the estimation of the unknown parameters by maximum pseudo-likelihood is discussed. Further, a sandwich type matrix, which is a consistent estimator for the asymptotic covariance matrix of the maximum likelihood (ML) estimator is determined. Two applications for real data of the proposed bivariate distribution is provided for illustrative purposes.
When the Virtual Machine Wins over the Container: DBMS Performance and Isolation in Virtualized Environments

: Database management systems (DBMSs) are a common service in the cloud. Cloud providers employ virtualization to consolidate DBMSs for e ﬃ cient resource utilization and to isolate collocated DBMS workloads. To guarantee the service-level agreement (SLA), the disk I / O performance and its isolation are critical in DBMSs because they are inherently disk I / O intensive. This paper investigates the disk I / O performance and its isolation of MySQL on two major virtualization platforms, KVM and LXC. KVM provides the hypervisor-based virtualization (virtual machines) while LXC provides the operating-system-level virtualization (containers). Containers are widely believed to outperform virtual machines because of negligible virtualization overheads, while virtual machines are to provide stronger performance isolation. Contrary to the general belief, our results show that KVM outperforms LXC by up to 95% in MySQL throughput without compromising the isolation. Furthermore, LXC violates the performance isolation; a container given 30% share of the disk bandwidth consumes the 70% share. Our analysis reveals that the ﬁle system journaling, which is mandatory to maintain ﬁle system consistency, has negative impact on both the performance and its isolation in the container. Because of the sharing of the journaling module in containers, the journaling activities are serialized and bundled with each other, resulting in poor performance and violation of the performance isolation.
Shortest Path Solution of Trapezoidal Fuzzy Neutrosophic Graph Based on Circle-Breaking Algorithm

The shortest path problem is a topic of increasing interest in various scientific fields. The damage to roads and bridges caused by disasters makes traffic routes that can be accurately expressed become indeterminate. A neutrosophic set is a collection of the truth membership, indeterminacy membership, and falsity membership of the constituent elements. It has a symmetric form and indeterminacy membership is their axis of symmetry. In uncertain environments, the neutrosophic number can more effectively express the edge distance. The objectives in this study are to solve the shortest path problem of the neutrosophic graph with an edge distance expressed using trapezoidal fuzzy neutrosophic numbers (TrFNN) and resolve the edge distance according to the score and exact functions based on the TrFNN. Accordingly, the use of a circle-breaking algorithm is proposed to solve the shortest path problem and estimate the shortest distance. The feasibility of this method is verified based on two examples, and the rationality and effectiveness of the approach are evaluated by comparing it with the Dijkstra and Bellman algorithms.
Estimating High Betweenness Centrality Nodes via Random walk in Social Networks

: The betweenness centrality is a widely used property to identify important nodes in social networks. Several algorithms have been studied to e ﬃ ciently compute the top-k nodes with the highest betweenness centrality on a graph where all the data is available. However, all the graph data of real social networks are not typically available to third parties such as researchers or marketers, and hence, an estimation algorithm based on sampling the graph data is required. Accurately estimating the top-k nodes with the highest betweenness centrality from a small sample of a graph is a challenging task. First, the top-k nodes need to be included in the small sample. Second, nodes with the high betweenness centrality that is deﬁned on the whole graph need to be accurately identiﬁed from the small sample. We propose a random walk-based algorithm to estimate the top-k nodes with the highest betweenness centrality by utilizing the ego betweenness centrality that has a high correlation with the betweenness centrality in social networks. The proposed algorithm ﬁrstly obtains a small sample that includes many of top-k nodes with the highest betweenness centrality via a random walk on a social network. Then, we obtain unbiased estimates of the ego betweenness centrality of sampled nodes and approximate the top-k nodes with the highest betweenness centrality as the top-k nodes with the highest estimated ego betweenness centrality. The proposed estimator e ﬃ ciently estimates the ego betweenness centrality of each sample without additionally sampling the graph data by utilizing the neighbor data of the previous and the next samples. The experiments using real social network datasets show that the proposed algorithm estimates more accurately the top-k nodes with the highest betweenness centrality than existing algorithms when the sample size is small.
A Discriminative Long Short Term Memory Network with Metric Learning Applied to Multispectral Time Series Classification

In this article, we propose an end-to-end deep network for the classification of multi-spectral time series and apply them to crop type mapping. Long short-term memory networks (LSTMs) are well established in this regard, thanks to their capacity to capture both long and short term temporal dependencies. Nevertheless, dealing with high intra-class variance and inter-class similarity still remain significant challenges. To address these issues, we propose a straightforward approach where LSTMs are combined with metric learning. The proposed architecture accommodates three distinct branches with shared weights, each containing a LSTM module, that are merged through a triplet loss. It thus not only minimizes classification error, but enforces the sub-networks to produce more discriminative deep features. It is validated via Breizhcrops, a very recently introduced and challenging time series dataset for crop type mapping.
A Review on Computer Vision-Based Methods for Human Action Recognition

Human action recognition targets recognising different actions from a sequence of observations and different environmental conditions. A wide different applications is applicable to vision based action recognition research. This can include video surveillance, tracking, health care, and human–computer interaction. However, accurate and effective vision based recognition systems continue to be a big challenging area of research in the field of computer vision. This review introduces the most recent human action recognition systems and provides the advances of state-of-the-art methods. To this end, the direction of this research is sorted out from hand-crafted representation based methods including holistic and local representation methods with various sources of data, to a deep learning technology including discriminative and generative models and multi-modality based methods. Next, the most common datasets of human action recognition are presented. This review introduces several analyses, comparisons and recommendations that help to find out the direction of future research.
A Survey of Domain Adaptation for Machine Translation

: Neural machine translation (NMT) is a deep learning based approach for machine translation, which out-performs traditional statistical machine translation (SMT) and yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available. Although a high-quality and domain-speciﬁc translation is crucial in the real world, domain-speciﬁc corpora are usually scarce or nonexistent, and thus vanilla NMT performs poorly in such scenarios. Domain adaptation that leverages both out-of-domain parallel corpora as well as mono-lingual corpora for in-domain translation, is very important for domain-speciﬁc translation. In this paper, we give a comprehensive survey of the state-of-the-art domain adaptation techniques for MT. Because of the current dominance of NMT in MT research, we give a brief review of domain adaptation for SMT, but put most of our e ﬀ ort into the survey of domain adaptation for NMT. We hope that this paper will be both a starting point and a source of new ideas for researchers and engineers who are interested in domain adaptation for MT.
Progressive Teaching Improvement For Small Scale Learning: A Case Study in China

Learning data feedback and analysis have been widely investigated in all aspects of education, especially for large scale remote learning scenario like Massive Open Online Courses (MOOCs) data analysis. On-site teaching and learning still remains the mainstream form for most teachers and students, and learning data analysis for such small scale scenario is rarely studied. In this work, we first develop a novel user interface to progressively collect students’ feedback after each class of a course with WeChat mini program inspired by the evaluation mechanism of most popular shopping website. Collected data are then visualized to teachers and pre-processed. We also propose a novel artificial neural network model to conduct a progressive study performance prediction. These prediction results are reported to teachers for next-class and further teaching improvement. Experimental results show that the proposed neural network model outperforms other state-of-the-art machine learning methods and reaches a precision value of 74.05% on a 3-class classifying task at the end of the term.
Viscous Matter in FRW Cosmology

We investigate the dynamics of dust matter with bulk viscosity effects. We explored the analogy dynamical problem to Chaplygin gas. Due to this analogy we give exact solutions for the FRW cosmology with viscosity coefficient parameterized by the Belinskii–Khalatnikov power law dependence with respect to energy density. These exact solutions are given in the form of hypergeometrical functions. We proved simple theorem which illustrated as viscosity effects can solved the initial singularity problem present in standard cosmological model.
Hand Gesture Recognition Based on Computer Vision: A Review of Techniques

Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture techniques and introduces their merits and limitations under different circumstances. In addition, it tabulates the performance of these methods, focusing on computer vision techniques that deal with the similarity and difference points, technique of hand segmentation used, classification algorithms and drawbacks, number and types of gestures, dataset used, detection range (distance) and type of camera used. This paper is a thorough general overview of hand gesture methods with a brief discussion of some possible applications.