Miracle-dz/UniFAR-FTUs huggingface dataset:
Tractable Coverage Analysis for Hexagonal Macrocell-Based Heterogeneous UDNs With Adaptive Interference-Aware CoMP

We consider a heterogeneous ultra dense network (HUDN) with both the hexagon and Poisson point process (PPP) layouts, which is more relevant for practical scenarios. A user-centric and adaptive interference-aware non-coherent coordinated multi-point transmission (IA-CoMP) scheme is used as a system setup to reduce both the cross-tier and the co-tier inter-cell interference (ICI) for the HUDN with range expansion (RE) in small cells. Due to the involvement of hexagonal macrocells, it is intractable to analyze the coverage performance of HUDNs with IA-CoMP. To this end, we present a mobile station GrouPing (MSGP)-based coverage analysis method, which partitions all MSs into four groups according to their main interference, and the whole coverage is obtained as the sum of the coverage of each MS group. We demonstrate that the proposed MSGP-based coverage analysis method can provide a tight upper bound when compared with Monte Carlo simulations. As the small cell density increases, the system coverage of HUDNs with hexagonal macrocells reduces exponentially, while the system coverage of HUDNs with PPP-based macrocells remains unchanged. Moreover, the system coverage increases with the larger one of the main ICI judging coefficient and the RE bias.
Design and Analysis of Distributed Multi-Agent Saddle Point Algorithm Based on Gradient-Free Oracle

In the paper, we are interested in one convex-concave function problem in network applications. Motivated by the saddle-point subgradient methods, we deal with a kind of saddle-point problem for multi-agent systems whose objective function for the underlying issue must be non-smooth but Lipschitz continuous. With the convex constrain set and global convex inequality constraints, we present a kind of distributed gradient-free algorithm in order to solve the issue of multi-agent convex-concave optimization. Under Slater’s condition, We give the results of convergence rate and the effect of smoothing parameters on error bounds.
Calibration of AOTF-Based Stereoscopic Imager for Measuring a Three-Dimensional Structure in Arbitrary Narrow Spectral Bands

We present a comparison of two methods of geometrical calibration for an acousto-optical stereoscopic spectral imager using a flat calibration target. It is shown that the method using a translation of a test object with a given step provides better accuracy of three-dimensional coordinates measurements with fewer images than the method using arbitrary displacements of the test object.
Impact of Particles Surface Functionalization on Interphase Properties of PI/Si3N4 Nanocomposites Using AFM

In the electrical insulation field, polymer nanocomposites (NC) organic/inorganic hybrid materials assure a distinct improvement of their macroscopic dielectric properties. These improvements are related to the nanoparticle/matrix interphase, a region where the presence of the nanoparticle changes the matrix properties. Moreover, recent studies show that a surface functionalization allows a better dispersion of the particles within the host matrix. This better dispersion plays a major role in the nanocomposite improvement properties. The aim of this work is to present the effect of the surface functionalization of spherical silicon nitride (Si3N4) nanoparticles on the interphase regions within a polyimide (PI) matrix. For this reason, mechanical (Young modulus) and electrical (permittivity) interphase nanoscale properties are measured using the quantitative nano mechanical (PF QNM) and the electric force microscopy (EFM) modes of the atomic force microscopy (AFM). These mechanical and electrical quantitative results allow us to compare the interphase dimension and properties between treated and untreated Si3N4 nanoparticles in order to correlate them to macroscopic properties modification.
Semi-Supervised Multi-Label Feature Selection based on Sparsity Regularization and Dependence Maximization

Multi-label learning deals with data samples that are simultaneously related to multiple labels. Likewise traditional single label learning, it is also difficult for multi-label learning to deal with high dimensional data. Feature selection is an effective solution for this difficulty via selecting a smaller subset from original features. Supervised multi-label feature selection techniques ask for sufficient labeled training data, however, the labeled data is often scarce. Several semi-supervise techniques which utilize labeled data and unlabeled data simultaneously have also been proposed. These semi-supervised solutions mainly use sparsity regularization and manifold assumption, as such they fail to take full advantage of the relation between features and labels. To make better use of the limited labels information to effectively select features, we introduced a semi-supervised multi-label Feature Selection algorithm based on sparsity regularization and dependence maximization (FSSRDM in short). FSSRDM utilizes the Hilbert-Schmidt Independence Criterion (HSIC) to capture the dependence between the features and labels, and adopt the $\ell_{2,1}$ sparsity term to obtain the regression coefficient sparse matrix. Experimental results show that FSSRDM is more effective than some state-of-art feature selection methods.
Proximity-Aware Location Based Collaborative Sensing for Energy-Efficient Mobile Devices

A fundamental question in the study of location-based mobile sensing is how much energy can be saved while still guaranteeing the reliable localization accuracy. In this paper, we analyze key features of human proximity and find a motivation which implies that the energy-efficient and accurate localization is possible by sharing the locations of nearby mobile devices. From the location-based collaborative sensing idea, we formulate an optimization problem which aims to minimize the total number of location measurements for a given fairness criterion. Then, we propose a practical and distributed location sharing (DLS) protocol and an optimal parameter control algorithm (OWD) which makes the DLS protocol attain an asymptotic optimal performance. Via extensive simulations under various environments including real mobility traces, we verify that the proposed DLS+OWD policy significantly reduces the average power consumption of mobile devices with a higher fairness compared to the existing algorithms.
Correlations Between Superconducting Characteristics and Structure of MgB2-Based Materials, ab-Initio Modeling

The influence of technological parameters (composition of precursor powders, type of additions, temperature, and pressure) on the superconducting characteristics (transition temperature, critical current density, and critical magnetic fields) of MgB2-based materials (bulk, wires, and thin films) and the correlation with their microstructure were studied. Microstructural observations are in good agreement with ab-initio modeling. The X-ray, Auger spectroscopy, and SEM EDX study show that impurity oxygen and carbon (possibly as dopant) are present in the magnesium diboride structure. The elements are distributed inhomogeneously, thus creating the nanostructure (which can be purposefully changed by manufacturing conditions) responsible for flux pinning and other SC characteristics.
Distributed H∞-Consensus Fault Detection for Fuzzy Systems with Faults, Switching Network Topology, Packet Dropouts and Channel Fading

This paper is concerned with the distributed H∞-consensus fault detection (FD) filtering problem for a class of discrete-time Takagi-Sugeno (T-S) fuzzy systems with faults, switching network topology, channel fading and different communication channels-induced packet dropouts with different missing rates. The purpose of the addressed problem is to design a distributed H∞-consensus FD filter to guarantee the sensitivity of the residual signal to the faults and the robustness of the residual system to effects of both switching network topology, channel fading and different communication channels-induced packet dropouts with different lossing rates. On the basis of the T-S fuzzy approach and the Lyapunov functional, distributed H∞-consensus FD filter design criterion is derived such that the residual system is exponentially stable in the mean square, and the optimal H∞ filtering performance index is derived. A simulation example is conducted to verify the usefulness of the proposed FD filter design approach.
End-to-end Answer Selection via Attention-Based Bi-LSTM Network

Many people ask medical questions online, finding the most suitable answer from candidate answers is an important research area in health care. The IEEE HotICN Knowledge Graph Academic Competition given a question and several candidate answers, then sort the candidate answers to get the best answer. We treated this subtask as a binary classification task, sorted the answers by calculating similarity between the question and each answer. In this work, we proposed a neural selection model trained on the training dataset. Our network architecture is based on the combination of Bi-LSTM and Attention mechanism, extended with biomedical word embeddings. Based on this fact, our model achieve state-of-the-art results on answer selection of medical community.
Power Allocation for Non-Orthogonal Millimeter Wave Systems With Mixed Traffic

We consider the problem of power allocation for the down-link of a 5G cellular system operating in the mm-wave band and serving two sets of users: fix-rate (FR) users (transmitting data at fixed rate), typically seen in device-to-device communications, and variable-rate (VR) users (that can change their transmission data rate), typically requiring high data rate services. The power allocation objective is the maximization of the spectral efficiency of VR users while ensuring that FR users get the required rate. In contrast with the existing literature on power allocation, we exploit the sparsity of the virtual mm-wave channel matrix, obtained by applying fixed discrete-Fourier transform beamformers at both the transmitter and the receiver. Exploiting the channel partial orthogonality, users are first grouped based on the mutual interference and then the power is allocated among and within groups.
Write-Aware Data Allocation on Heterogeneous Memory Architecture with Minimum Cost

More and more Non-Volatile Memories (NVM) have been widely applied to various embedded systems to build the heterogeneous memory architecture. However, the write-endurance of NVM remains a great challenge. Hence, we should take full consideration of the write-endurance of NVM when allocating data on heterogeneous memory architecture. There is an observation that, for most real workloads, about 10% of data account for 90% write operations. This brings us an opportunity to reduce the write wear of NVM through carefully allocating write-intensive data. In this paper, we explore the problem that how to find a balance between the system cost and write-endurance of NVM for data allocation on heterogeneous memory architecture. We propose a write-aware data allocation algorithm, WADA. WADA can not only greatly reduce the write wear of NVM, but also guarantee the near-optimal system cost. We also propose an integer linear programming (ILP) model to generate an optimal data allocation, which can obtain the minimum cost. The result of ILP can be used as a standard to evaluate the efficiency of other algorithms. Experiments show that WADA outperforms all the other algorithms on both system cost and write wear of NVM. Compared to previous algorithms, WADA can reduce up to 47.77% system cost and 60.89% write wear of NVM. Compared to ILP, WADA can achieve the near-optimal system cost within just 2% difference.
Voxel Map Based Collision Detection for Underwater Manipulators

This paper presents a real-time collision detection algorithm for underwater robotic manipulation. It is capable of detecting collisions between multiple manipulators and between manipulators and obstacles in the workspace. Assuming operations are typically carried out on artificial structures with a known geometry, it is possible to model the environment from a priori known CAD models. The position of objects in the workspace is tracked using a vision based approach that relies on fiducial marker detection. The proposed collision detection method is developed, integrated into a commercial ROV manipulator control system, and successfully evaluated in simulations and experimental setup using a real industry standard underwater manipulator.
Improving Phonetic Recognition with Sequence-length Standardized MFCC Features and Deep Bi-Directional LSTM

Phonetic recognition is one of the most challenging problems in the field of speech analysis. These applications can be mentioned such as dialect identification [1], mispronunciation detection [2], spoken document retrieval [3], and so on. There are different approaches to solve these problems such as improving the feature selection on input speech [4], applying deep learning technique [5] [6] [7] or combining both of them [8]. With the sequence data as the phonetics, the architecture which is based on recurrent neural network (RNN) is an appropriate approach [9]. It is even more powerful when combined with the improvement of features selection on input data. In our approach, we combine the Mel Frequency Cepstral Coefficients (MFCC) method with sequence-length to present the acoustic features of speech and use some RNN models to phonetic classification. Our experiments are implemented on the Texas Instruments Massachusetts Institute of Technology (TIMIT) [10] phone recognition dataset. Especially, our data processing and features selection method give consistently better results than other researches using the same neural network model. Currently, we have achieved the lowest error test rate (13.05%) by using Bidirectional LSTM, which is the best result in TIMIT dataset with the reduction of about 3.5% over the last best result [5] [6].
Développement d'implants actifs et nanostructurés à des fins de régénération dentaire

L’ingenierie tissulaire a vu emerger, dans la derniere decennie, la nanomedecineregenerative combinant non seulement les cellules souches mais aussi les facteursde croissance. Le but de ce travail a ete d’utiliser les techniques de l’ingenierieosseuse et pulpaire pour ameliorer ou creer des implants actifs et vivants.L’ingenierie tissulaire vise a remplacer ou a reparer des tissus endommages del’organisme par des implants bioactifs imitant les tissus naturels. Ainsi, il est necessairede developper en premier lieu des materiaux biocompatibles, c’est-a-direacceptes par l’organisme, et capables d’interagir avec les cellules au site del’implantation du dit materiau afin d’aider a la fonction de l’implant. Pour cette raison,de nombreuses methodes visent le developpement de biomateriaux implantablesimitant le mieux possible la matrice extracellulaire. Dans cette etude,l’"electrospinning" a ete utilise afin de concevoir des membranes nanofibreuses. Ellesont ensuite ete fonctionnalisees et associees a des germes de 1re molaire mandibulairede souris afin d'en favoriser le developpement. Apres une revue de litteratures'interessant a l'ingenierie tissulaire en chirurgie dentaire, deux axes de rechercheseront developpes. Le premier concerne la regeneration de l’unite os/dent et le deuxiemel'innervation de la dent.
Characterization of the Electrical Behaviour of Thin Dielectric Films at Nanoscale using Methods Derived from Atomic Force Microscopy: Application to Plasma Deposited Agnps-Based Nanocomposites

Recent advances in the development of micro-and nano-devices call for applications of thin nanocomposite dielectric films (thickness less than few tens of nanometers) with tuneable electrical properties. For optimization purposes, their behaviour under electrical stress needs to be probed at relevant scale, i.e. nanoscale. To that end electrical modes derived from Atomic Force Microscopy (AFM) appear the best methods due to their nanoscale resolution and non-destructive nature which permits in-situ characterization. The potentialities of electrical modes derived from AFM are presented in this work. The samples under study consist of plasma processed thin dielectric silica layers with embedded silver nanoparticles (AgNPs). Charge injection at local scale, performed by using AFM tip, is investigated by Kelvin Probe Force Microscopy (KPFM). Modulation of the local permittivity induced by the presence of AgNPs is assessed by Electrostatic Force Microscopy (EFM).
Towards a Machine Learning Approach for Detecting Click Fraud in Mobile Advertizing

In recent years, mobile advertising has gained popularity as a mean for publishers to monetize their free applications. One of the main concerns in the in-app advertising industry is the popular attack known as “click fraud”, which is the act of clicking on an ad, not because of interest in this ad, but rather as a way to generate illegal revenues for the application publisher. Many studies evaluated click fraud attacks in the literature, and some proposed solutions to detect it. In this paper, we propose a click fraud detection model, hereafter CFC, to classify fraudulent clicks by adopting some features and then testing using KNN, ANN and SVM. In fact, based on our experimental results, the different featured classifiers reached an accuracy higher than 93%.
Acceleration of Depth Intra Coding for 3D-HEVC by Efficient Early Termination Algorithm

3D extension of High Efficiency Video Coding (3D-HEVC) is the newest international standard for 3D video coding. Compared to conventional HEVC, the encoding materials consist of multi-view color textures and associated depth maps. However, the advanced techniques enhance the coding efficiency at the cost of the computation complexity and time consumption. In this paper, we propose an early termination algorithm to speed-up the encoding procedure. With the complexity check by total sum of squares (TSS) and the RD-cost criterion, we effectively accelerate the depth intra coding of 3D-HEVC and maintain the video quality. The experimental results show that both of the time-saving and the BD-BR performance of our method outperform previous works.
Optical Character Recognition for Handwritten Mathematical Expressions in Educational Humanoid Robots

Humanoid robots are seen as perfect education partners for students these days. Many researchers are working on educational humanoid robots to make them more effective in educating students. One of many approaches to realize it is to make the robots educate children in two-way communication. In this case, robots can recognize students’ progress and provide suitable learning materials according to their progress. In a simple scenario for basic mathematics learning, the robot educates a student on how to solve mathematical expressions. As the student understand the concepts, the robot gives some simple exercises to further improve student’s ability. While completing the exercise, the partner robot supervises each mathematical expression solved by the student, acknowledges mistakes and provides correct solutions. Thus, the robot must be equipped with a system which can recognize mathematical expressions and give the solutions. Such system is an inherent foundation to build much smarter robots in delivering education to students. This paper covers detail design and implementation of the system which recognizes handwritten mathematical equations and provides the corresponding solutions. Testing results show that the system performs quite well with 97.4% accuracy with slight errors for characters with similar shapes.
Dynamic Area Search with Shared Memory: A Meta-Framework to Improve Pathfinding Algorithms

Finding the shortest path between two given objects/states is a common problem for many scenarios/applications. Although many algorithms have been proposed, most of them rely entirely on the heuristic metrics to guide the search for the optimal path. In this work, we proposed a novel and generic approach to learn the underlying structure of the environment while exploring the problem seamlessly. The approach, Dynamic Area Search with Shared Memory (DASSM), learns from already explored areas in the pathfinding problem and efficiently and dynamically reuse the information to guide the utilized pathfinding algorithms. We showed how DASSM can alleviate the computational overhead by limiting and focusing the search to regions that more likely have the optimal path based on the learned information. In addition, we elaborated on the implementation and technical details of the approach and revealed its feasibility to be implemented to a wide range of informed search algorithms. To test DASSM, we applied it for three common pathfinding algorithms and tested them on publicly available benchmarks. DASSM improved the performance in all cases and reduced the execution time up to 75%. Moreover, we examined adding random steps for DASSM, where the results revealed a potential improvement in the execution time.
Predicting Employee Attrition using Machine Learning

The growing interest in machine learning among business leaders and decision makers demands that researchers explore its use within business organisations. One of the major issues facing business leaders within companies is the loss of talented employees. This research studies employee attrition using machine learning models. Using a synthetic data created by IBM Watson, three main experiments were conducted to predict employee attrition. The first experiment involved training the original class-imbalanced dataset with the following machine learning models: support victor machine (SVM) with several kernel functions, random forest and K-nearest neighbour (KNN). The second experiment focused on using adaptive synthetic (ADASYN) approach to overcome class imbalance, then retraining on the new dataset using the abovementioned machine learning models. The third experiment involved using manual undersampling of the data to balance between classes. As a result, training an ADASYN-balanced dataset with KNN (K = 3) achieved the highest performance, with 0.93 F1-score. Finally, by using feature selection and random forest, F1-score of 0.909 was achieved using 12 features out of a total of 29 features.
Method for Coloring Night-vision Imagery Based on Multispectral Semantic Segmentation

Color night vision can map natural colors to nighttime images of multiple bands (e.g., visible and long-wave infrared (LWIR)). These colors can assist the observers in better and faster understanding images, thus improving their situational awareness and shortening the reaction time. In this paper, we present an effective method combining deep learning and category colors. It utilizes the semantic segmentation for image segmentation first, and then colorize the image according to categories to avoid the same color scheme and unnatural colors. We compare our method with some others quantitatively and qualitatively, such as global colorization by single lookup table, where we show significant improvements. In addition, it can be expanded according to different environments and applications because of the fixed category colors.