Miracle-dz/UniFAR-FTUs huggingface dataset:
mIot Connectivity Solutions for Enhanced 5G Systems

Within the ongoing activities devoted to the definition of 5G networks, massive Internet of Things (mIoT) is regarded as a compelling use case, both for its relevance from business perspective, and for the technical challenges it poses to network design. With their envisaged massive deployment of devices requiring sporadic connectivity and small data transmission, yet QoS constrained, mIoT services will require adhoc end- to-end (E2E) solutions, i.e., featuring access and core network enhanced Control and User planes (CP/UP) mechanisms. This paper presents and evaluates a novel connectivity solution to manage massive number of devices. The paper presents an analytical model developed to evaluate the performance of the proposed solution. Quantitative results derived from the model demonstrate the effectiveness of the solution proposed in this paper, compared to 4G systems, and its ability to reduce CP signaling and optimize UP resource utilization for massive device deployment.
Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory

The multicategory SVM (MSVM) of Lee et al. (2004) is a natural generalization of the classical, binary support vector machines (SVM). However, its use has been limited by computational difficulties. The simplex-cone SVM (SCSVM) of Mroueh et al. (2012) is a computationally efficient multicategory classifier, but its use has been limited by a seemingly opaque interpretation. We show that MSVM and SCSVM are in fact exactly equivalent, and provide a bijection between their tuning parameters. MSVM may then be entertained as both a natural and computationally efficient multicategory extension of SVM. We further provide a Donsker theorem for finite-dimensional kernel MSVM and partially answer the open question pertaining to the very competitive performance of One-vs-Rest methods against MSVM. Furthermore, we use the derived asymptotic covariance formula to develop an inverse-variance weighted classification rule which improves on the One-vs-Rest approach.
Mobile Edge Computing for Task Offloading in Small-Cell Networks via Belief Propagation

A large number of computation-hungry mobile applications have led to an ever-increasing computation demands. Mobile edge computing (MEC) has been considered as an emerging paradigm to alleviate the demand effectively by offloading the computationally intensive tasks from mobile devices (MD) to the adjacent MEC servers. It is expected that the quality of computation experience, e.g., the computing energy and the execution latency, can be greatly improved by the MEC. In this paper, we will investigate the computing task offloading problem via the MEC in the context of small-cell base-station (SBS) networks, where each SBS is equipped with an MEC server. Specifically, we first formulate the optimization problem to minimize the objective, i.e., the weighted sum of energy consumption and execution duration. The parameters to be optimized are the allocations of the MD's tasks to be offloaded to the MEC servers. Then we propose a novel belief propagation (BP) algorithm to optimize the task allocation in a distributed manner. Next, we develop the factor graph according to the network topology and decompose the object function into multiple local utilities to fit the factor graph. Finally, we transform local utilities into the estimations of marginal distributions and propose a distributed BP algorithm to solve the estimations. Simulations demonstrate that our BP algorithm can effectively approach the optimal solutions via exhaustive search.
Localizing Access Point Through Simple Gesture

It is important to obtain the location of an access point (AP), such as in the scenario of finding rogue AP in public places or designing indoor positioning system based on AP locations. Most of the existing AP localization methods depend on additional equipment with high cost or the path loss model suffering from indoor multipath. In this paper, an AP localization algorithm based on channel state information and Fresnel zone is proposed, which can just draw a semicircle with one hand to estimate the AP location. The algorithm is divided into two phases: preparations and positioning. Preparations’ phase includes calibrating AP direction and identifying Fresnel zone cutting points which are obtained by combining the least squares fitting method in the time domain. Positioning phase consists of AP direction determination and AP position determination; the final AP position is confirmed according to the geometrical information. The experimental results show that the median error of our algorithm can achieve to 0.58 m, which significantly improve the AP positioning performance than other methods.
QoS-Aware Heuristic Scheduling with Delay-Constraint for WBSNs

Wireless body sensor networks (WBSNs), which efficiently and intelligently sense the physiological signals of the medical patients to support various medial applications, have allured tremendous attention from various research communities. For energy and resource constrained WBSNs, the important issues include: 1)~dynamic channel characteristics due to mobility and postural dynamics; 2) high energy efficiency owing to limited battery power; 3) high quality-of- service (QoS) requirement due to critical physiological data. To address the above issues, a cost-effective heuristic packet scheduling scheme is designed to provide the high network throughput and fair QoS to WBSNs. Unlike most of the existing works, we also consider the optimal delay- constraint in order to achieve the optimized packet transmission delay and to manage the heavy traffic load optimally. Specifically, we consider the critical factors of WBSNs to prioritize the data packets among access points, e.g., medical emergent patients have the higher priority to send their data packets than the normal patients. We formulate the proposed scheme mathematically. Simulation results are presented to demonstrate the effectiveness of the proposed heuristic packet scheduling scheme over other existing state-of- the-art solutions, in terms of packet transmission delay, cost and network throughput.
A Language Model based Evaluator for Sentence Compression

We herein present a language-model-based evaluator for deletion-based sentence compression and view this task as a series of deletion-and-evaluation operations using the evaluator. More specifically, the evaluator is a syntactic neural language model that is first built by learning the syntactic and structural collocation among words. Subsequently, a series of trial-and-error deletion operations are conducted on the source sentences via a reinforcement learning framework to obtain the best target compression. An empirical study shows that the proposed model can effectively generate more readable compression, comparable or superior to several strong baselines. Furthermore, we introduce a 200-sentence test set for a large-scale dataset, setting a new baseline for the future research.
Gerrymandering Over Graphs

In many real-life scenarios, voting problems consist of several phases: an overall set of voters is partitioned into subgroups, each subgroup chooses a preferred candidate, and the final winner is selected from among those candidates. The attempt to skew the outcome of such a voting system through strategic partitioning of the overall set of voters into subgroups is known as "gerrymandering''. We investigate the problem of gerrymandering over a network structure; the voters are embedded in a social network, and the task is to divide the network into connected components such that a target candidate will win in a plurality of the components. We first show that the problem is NP-complete in the worst case. We then perform a series of simulations, using random graph models incorporating a homophily factor. In these simulations, we show that a simple greedy algorithm can be quite successful in finding a partition in favor of a specific candidate.
A Cross-layer based mapping for spiking neural network onto network on chip

Abstract Network-on-Chip provides a packet-based and scalable inter-connected structure for spiking neural networks. However, existing neural mapping methods just distribute all neurons of a population into an on-chip network core or nearby cores sequentially. As there is no connection among population, the population based mapping degrades inter-neuron communicating performance between different cores. This paper presents a Cross-LAyer based neural MaPping method that maps synaptic connected neurons belonging to adjacent layers into the same on-chip network node. In order to adapt to various input patterns, the strategy also takes input spike rate into consideration and remap neurons for improving mapping efficiency. The method helps to reduce inter-core communication cost. The experimental results demonstrate the efficient results of the proposed mapping strategy in the aspect of spike transfer latency as well as dynamic energy cost improvement. In the applications of handwritten digits and edge extraction, in which the type of interconnection among neurons is different, the neural mapping algorithm reduces spike average transfer latency by maximum 42.83%, and reduces dynamic energy by maximum 36.29%. Graphical Abstract Cross-layer based mapping for spiking neural network onto network on chip.
MoCoNet: Motion Correction in 3D MPRAGE images using a Convolutional Neural Network approach

Purpose: The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper is to develop a standalone novel technique to suppress motion artefacts from MR images using a data-driven deep learning approach. Methods: A deep learning convolutional neural network (CNN) was developed to remove motion artefacts in brain MR images. A CNN was trained on simulated motion corrupted images to identify and suppress artefacts due to the motion. The network was an encoder-decoder CNN architecture where the encoder decomposed the motion corrupted images into a set of feature maps. The feature maps were then combined by the decoder network to generate a motion-corrected image. The network was tested on an unseen simulated dataset and an experimental, motion corrupted in vivo brain dataset. Results: The trained network was able to suppress the motion artefacts in the simulated motion corrupted images, and the mean percentage error in the motion corrected images was 2.69 % with a standard deviation of 0.95 %. The network was able to effectively suppress the motion artefacts from the experimental dataset, demonstrating the generalisation capability of the trained network. Conclusion: A novel and generic motion correction technique has been developed that can suppress motion artefacts from motion corrupted MR images. The proposed technique is a standalone post-processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for a multitude of MR sequences.
La puissance de désaccord d'un adversaire

Un des resultats fondamentaux de l'algorithmique distribuee est que le niveau d'accord qui peut etre obtenu en presence de $t$ pannes est exactement $t+1$. Autrement dit un adversaire qui peut mettre en panne n'importe quel sous ensemble d'au plus $t$ processus peut empecher les autres processus de se mettre d'accord sur $t$ valeurs. Mais quelle est la puissance des ($2^{2^n} -n$) autres adversaires qui ne peuvent mettre en panne que certaines combinaisons des processus? Cet article presente une caracterisation precise d'un adversaire. On y introduit la notion de "puissance de desaccord". La puissance de desaccord d'un adversaire est le plus grand entier $k$ tel que l'accord des processus sur $k$ valeurs soit possible avec cet adversaire. Puis on montre comment {\em calculer} automatiquement cet entier pour un adversaire donne.
Ancestral polymorphisms explain the role of chromosomal inversions in speciation

Understanding the role of chromosomal inversions in speciation is a fundamental problem in evolutionary genetics. Here, we perform a comprehensive reconstruction of the evolutionary histories of the chromosomal inversions in Drosophila persimilis and D. pseudoobscura. We provide a solution to the puzzling origins of the selfish Sex-Ratio arrangement in D. persimilis and uncover surprising patterns of phylogenetic discordance on this chromosome. These patterns show that, contrary to widely held views, all fixed chromosomal inversions between D. persimilis and D. pseudoobscura were already present in their ancestral population long before the species split. Our results suggest that patterns of higher genomic divergence and an association of reproductive isolation genes with chromosomal inversions may be a direct consequence of incomplete lineage sorting of ancestral polymorphisms. These findings force a reconsideration of the role of chromosomal inversions in speciation, not as protectors of existing hybrid incompatibilities, but as fertile grounds for their formation.
Sampling-based Rig Conversion into Non-rigid Helper Bones

While 3D animation packages provide a wide variety of animation rigs for creating expressive skin animation, most interactive systems employ linear blend skinning for hard realtime computation. We propose a method for converting an arbitrary skeleton-driven deformer into a linear blend skinning-based helper bone rig. Our system builds the target rig by applying an example-based skinning technique that uses a minimal training dataset obtained from the source model by two-pass sampling of the skin deformation. The first uniform sampling analyzes the relationship between the rotation of each joint and the deformation of skin vertices. The second sampling composes a minimum training dataset by selecting important pose samples using novel geometrical measures. We also propose a skinning decomposition with similarity transformation algorithm for accurately approximating the non-rigid skin deformation behavior by helper bone transformations. Our experimental results demonstrate the proposed automated rig conversion into non-rigid helper bones from several skeleton-driven deformers, including Delta Mush deformers, corrective blendshapes, and virtual-muscle systems.
Transferring and Animating a non T-pose Model to a T-pose Model

Non T-pose animation is a technique that attempts to generate natural transformations between any non T-pose skeletons to the neutral T-pose skeleton. It is not always easy to extract or embed a T-pose animation skeleton into a 3D human model in an arbitrary initial position. This is even more problematic for natural human models obtained by 3D scanning, especially models of babies and kids. In addition, transforming a non T-pose to a T-pose requires a large amount of calculations. Hence, many commercially available software do not provide efficient methods to standardize non T-pose skeletons. This paper focuses on developing a simplified transformation method, which enables skeletons in arbitrary poses to be standardized and used in other media conveniently.
Graphically Representing Child-Robot Interaction Proxemics

This paper discusses the current analysis of a large set of child-robot interaction experiments, in which a 2–4 year old child plays a set of games with a small humanoid robot, with the goal of detecting early signs of autism in toddlers based on the interactions. Our ﬁrst goal in this paper is to condense and display these child-robot interactions as multi-channel time series, starting with the distances between the child, robot, parent, and experimenter. Our second goal is to use these data displays to compare and contrast different children, with the aim of clustering children with similar interaction patterns. Using a ceiling-mounted camera, we record the interaction between a child and a robot which performs different games and dances. After analyzing the video footage for the locations of all people and the robot in the room over the variable length of the interaction, we condense the interactions into simpliﬁed, quantiﬁable, scale-invariant data. We show how the distances between the child and robot, experimenter, and caregiver can be discretized into a few location zones and compared across children using classic similarity measures. Proxemics (social distances) between the child, robot, care-giver, and experimenter during a child-robot interaction show differences between participants and hence can provide additional information for behavior analysis.
Application of Adaptive PD Control to a Biomechanics System Known as Exoskeleton

 Abstract—The control in biomechanical systems has become an active field of biomedical engineering. This paper describes a design of adaptive control to achieve desired in the system defined as exoskeleton moves, the adaptive method is located is selected taking into account energy savings because the adaptive controller is based on a scheme of variable gain in time Proportional and Derivative respect to PD control. The structure of adaptive gain is determined using a type of control Lyapunov function. The adaptation law uses velocity estimation based on a robust exact differentiator (RED) implemented as a variation of SuperTwisting algorithm. The derivative adaptive proportional controller is evaluated on simulated exoskeleton structure. The set of simulations considers the presence of an external disturbance. The controller proves efficient to counter the effects of external mechanical system. Proposed controller performance was superior to standard proportional derivative controller, and has been shown in this study.
CTOM: Collaborative Task Offloading Mechanism for Mobile Cloudlet Networks

Mobile cloud computing has emerged as a pervasive paradigm to execute computing tasks for capacity- limited mobile devices. More speciﬁcally, at the network edge, the resource-rich and trusted cloudlet system is acting as a 'data center in a box' to support compute-intensive mobile applications. The mobile cloudlets can provide in-proximity services by executing the workloads for nearby devices. Nevertheless, load balancing in mobile cloudlet network is of great importance, as it has a huge impact on task response time. Existing methods for cloudlet load balancing basically rely on the strategic placement or user cooperation. However, the above solutions require the global task load information from the whole network, which is costly in both communication and computation. To achieve more efﬁcient and low-cost load balancing, we propose 'CTOM', a Collaborative Task Ofﬂoading Mechanism for mobile cloudlet networks. Our solution is based on the balls-and-bins theory and can balance the task load only requiring limited information. Extensive simulations and evaluation based on mobility trace demonstrate that, our CTOM outperforms the conventional random and proportional allocation schemes by reducing the task gaps among mobile cloudlets by 65% and 55% respectively. Meanwhile, CTOM's performance is close to that of the greedy algorithm but with much lower computing complexity.
Pricing in discrete financial models

2 Filtrations 5 2.1 Basic definitions . 5 2.2 Stochastic processes . 7 2.2.1 Adapted stochastic processes . 7 2.2.2 Predictable stochastic processes . 8 2.3 Initially trivial filtrations . 10 2.4 Filtration-equivalent measure spaces . 11
On the performance of the poisson and the negative binomial ridge predictors

ABSTRACT Mansson and Shukur (2011) investigated the performance of the Poisson ridge regression (PRR) estimator in terms of the mean square error (MSE) criterion. Similarly, Mansson (2012) investigated the performance of the Negative binomial ridge regression (NBRR) according to the MSE criterion. But there is no any analysis of the predictive performance of the PRR and NBRR estimators. Therefore, we define the PRR and the NBRR predictors to evaluate their predictive performances according to the prediction mean squared error under the target function. The Monte Carlo simulations and the real life numerical example are conducted to investigate the defined predictors' performance.
Microring-Based Si/SiN Dual-Layer Switch Fabric

The first microring-based Si/SiN dual-layer switch fabric is fabricated, packaged and characterized. The 4×4 thermally-actuated switch fabric implements the switch-and-select architecture in an ultra-compact footprint. It leverages the Si/SiN dual-layer structure achieving a crossing-free design, showing great potential for ultra-low loss and crosstalk switching applications.
Development of a Quad-rotor Fixed-wing Hybrid Unmanned Aerial Vehicle

This paper describes the development of a quadrotor fixed-wing hybrid unmanned aerial vehicle (UAV). By combining the flying qualities of quadrotor UAV and that of fixed-wing UAVs it is possible to achieve superior flying qualities. This way, the vertical take-off and landing capability of quadrotors and the long endurance of fixed-wing UAVs have been put together, while eliminating the disadvantages of both of these UAV types. The proposed hybrid drone has shown exciting performance in taking-off, cruising and landing fully autonomously. Control transition from quadrotor to fixed-wing and backwards have been analyzed. Sky Scout fixed-wing drone has been used to build the hybrid UAV, and the performance has been successfully demonstrated through flight tests. This paper precent the complete design of a quadrotor fixed-wing hybrid aerial vehicle with modelling, simulation and experimental verification.
A Mobile Computing Method Using CNN and SR for Signature Authentication with Contour Damage and Light Distortion

A signature is a useful human feature in our society, and determining the genuineness of a signature is very important. A signature image is typically analyzed for its genuineness classification; however, increasing classification accuracy while decreasing computation time is difficult. Many factors affect image quality and the genuineness classification, such as contour damage and light distortion or the classification algorithm. To this end, we propose a mobile computing method of signature image authentication (SIA) with improved recognition accuracy and reduced computation time. We demonstrate theoretically and experimentally that the proposed golden global-local (G-L) algorithm has the best filtering result compared with the methods of mean filtering, medium filtering, and Gaussian filtering. The developed minimum probability threshold (MPT) algorithm produces the best segmentation result with minimum error compared with methods of maximum entropy and iterative segmentation. In addition, the designed convolutional neural network (CNN) solves the light distortion problem for detailed frame feature extraction of a signature image. Finally, the proposed SIA algorithm achieves the best signature authentication accuracy compared with CNN and sparse representation, and computation times are competitive. Thus, the proposed SIA algorithm can be easily implemented in a mobile phone.