Miracle-dz/UniFAR-FTUs huggingface dataset:
LARAC-SN and Mole in the Hole: Enabling Routing through Service Function Chains

With the emergence of Software-Defined Networking (SDN) and Network Function Virtualization (NFV), the problem of centralized routing through intermediate specified nodes (for which several candidates can be defined) has become an important issue. Indeed, an SDN controller routing flows through Service Function Chains (SFCs) has to efficiently solve this problem to achieve the online provisioning of routing requests and the online placement of Virtual Network Functions (VNFs). In this paper, we propose two algorithms for solving this problem. First, we propose LARAC for specified nodes (LARAC-SN), a fast and close to optimal algorithm for finding the constrained shortest path (CSP) visiting an ordered set of specified nodes. Second, we propose Mole in the Hole (MITH), a graph transformation algorithm which can force any state-of-the-art routing algorithm to visit an ordered set of specified nodes. While LARAC-SN is bounded to the specific CSP problem and can only handle one candidate per specified node, MITH can be used for any routing problem and can deal with several candidates per specified node. Through evaluations, we show that LARAC-SN is fast and close to optimal (its optimality gap stays lower than 1.62% in average) and that MITH has the potential of reaching optimality for any problem, but at the cost of a higher runtime.
Robust Rough-Terrain Locomotion with a Quadrupedal Robot

Robots working in natural, urban, and industrial settings need to be able to navigate challenging environments. In this paper, we present a motion planner for the perceptive rough-terrain locomotion with quadrupedal robots. The planner finds safe footholds along with collision-free swing-leg motions by leveraging an acquired terrain map. To this end, we present a novel pose optimization approach that enables the robot to climb over significant obstacles. We experimentally validate our approach with the quadrupedal robot ANYmal by autonomously traversing obstacles such steps, inclines, and stairs. The locomotion planner re-plans the motion at every step to cope with disturbances and dynamic environments. The robot has no prior knowledge of the scene, and all mapping, state estimation, control, and planning is performed in real-time onboard the robot.
MomentSA: A Fast and Accurate Method for Stochastic Kronecker Graph Parameter Computing

Stochastic Kronecker Graph model is widely used to generate synthetic graphs that simulate real-world graphs. In this model, the initiator matrix decides the degree to which the synthetic graph approximates the real-world graph. The computing of initiator matrix, however, requires that the number of the nodes of input graph is the power of the dimension of the initiator matrix. In order to fulfill such requirement, some methods (e.g., KronFit and Moment) add isolated nodes to the input graph, which damages the input graph's properties. Other method (e.g., KronEM) predicts the links between the nodes after adding isolated nodes. Unfortunately, the prediction dramatically increases the complexity of computing. In this paper, we propose a method named as MomentSA to solve the problems. Our method completes the input graphs by leveraging the law of property changes of graphs, which does not need to predict the links as method KronEM. Simultaneously, our method uses the moment-based estimation and ADAM (Adaptive Moment Estimation) method to compute the initiator matrix, which can compute the initiator matrix quickly. Experiment results on our prototype implementation suggest that the initiator matrix computed from our method is more accurate than existing state-of-art systems, and the speed of computing is about three to four orders of magnitude faster than state-of-art systems.
MRI Tumor Detection and Localization by Multiple Threshold Object Counting Technique

Human brain tumor is a major health issue that may cause severe disabilities to a patient, ranging from loss of sensory feelings and paralysis to death. The accurate diagnosis of brain tumors that includes detection and localization is an essential procedure for effective treatment planning. Despite the fact that many brain tumor segmentation techniques have been developed in the past, effective tumor segmentation is still challenging because of the complex nature of the brain MRI image. It is difficult to differentiate between the tumor, which appears in the MRI image as a bright structure, and other bright structures that are not necessarily tumors. To address this problem, we propose a novel effective tumor segmentation method for brain MRI images. This technique is based on multiple threshold and object counting technique that can accurately detect the position of the tumor by being able to eliminate other similar structures.
Edge Computing in IoT-Based Manufacturing

Edge computing extends the capabilities of computation, network connection, and storage from the cloud to the edge of the network. It enables the application of business logic between the downstream data of the cloud service and the upstream data of the Internet of Things (IoT). In the field of Industrial IoT, edge computing provides added benefits of agility, real-time processing, and autonomy to create value for intelligent manufacturing. With the focus on the concept of edge computing, this article proposes an architecture of edge computing for IoT-based manufacturing. It also analyzes the role of edge computing from four aspects including edge equipment, network communication, information fusion, and cooperative mechanism with cloud computing. Finally, we give a case study to implement the active maintenance based on a prototype platform. This article aims to provide a technical reference for the deployment of edge computing in the smart factory.
Do the basic crystal chemistry principles agree with a plethora of recent quantum chemistry data?

Electron descriptors such as bond order and electron density at the bond critical point are key quantities in establishing the characterization of chemical bonding. This paper analyzes a plethora of quantum chemistry data available in the literature to confirm exponential correlations between these electron descriptors and the interatomic distances for any type of chemical bond. This is particularly relevant for extending the crystal chemistry bond order conservation principle to systems with delocalized electrons.
The Dubins Car and Other Arm-Like Mobile Robots

This paper investigates the connection between the kinematics of robots arms and the shortest paths for mobile robots. Lagrange multipliers are used to show that the shortest paths are equivalent to arms in configurations that balance an external force, while applying equal torques and forces at each joint. Analysis of the arm Jacobian yields a further geometric interpretations of optimal paths, constraining the locations of rotation centers and the directions of translations that may occur along optimal paths.
Manifest the Invisible: Design for Situational Awareness of Physical Environments in Virtual Reality

Virtual Reality (VR) provides immersive experiences in the virtual world, but it may reduce users' awareness of physical surroundings and cause safety concerns and psychological discomfort. Hence, there is a need of an ambient information design to increase users' situational awareness (SA) of physical elements when they are immersed in VR environment. This is challenging, since there is a tradeoff between the awareness in reality and the interference with users' experience in virtuality. In this paper, we design five representations (indexical, symbolic, and iconic with three emotions) based on two dimensions (vividness and emotion) to address the problem. We conduct an empirical study to evaluate participants' SA, perceived breaks in presence (BIPs), and perceived engagement through VR tasks that require movement in space. Results show that designs with higher vividness evoke more SA, designs that are more consistent with the virtual environment can mitigate the BIP issue, and emotion-evoking designs are more engaging.
Text-to-Speech Synthesis Using STFT Spectra Based on Low-/Multi-Resolution Generative Adversarial Networks

This paper proposes novel training algorithms for vocoder-free statistical parametric speech synthesis (SPSS) using short-term Fourier transform (STFT) spectra. Recently, text-to-speech synthesis using STFT spectra has been investigated since it can avoid quality degradation caused by the vocoder-based parameterization in conventional SPSS using a vocoder. In conventional SPSS using a vocoder, we previously proposed a training algorithm for integrating generative adversarial network (GAN)-based distribution compensation. To extend the algorithm to vocoder-free SPSS, we propose low- and multi-resolution GAN-based training algorithms for vocoder-free SPSS. In our algorithm that uses the low-resolution GAN, acoustic models are trained to minimize the weighted sum of the mean squared error between natural and generated spectra in the original resolution and adversarial loss to deceive discriminative models in the lower resolution. Since the low-resolution spectra are close to filter banks and their distribution becomes simpler, GAN-based distribution compensation works well. Furthermore, we propose an algorithm using multi-resolution GANs, which uses both the low-resolution GAN and original-resolution GAN. Experimental results demonstrate that 1) the low-resolution GAN works robustly to the setting of its frequency resolution and hyperparameter, and 2) compared the low-, original-, and multi-resolution GANs, the low-resolution GAN works the best to improve synthetic speech quality.
Reliability Aware Real Time Scheduling Strategies for Heterogeneous Embedded Systems

As the design paradigm shifts from federated to integrated architecture in the automotive domain [1], scheduling of real time tasks becomes increasingly important to utilize the computation resources of integrated architectures. In addition to having real time deadlines, automotive tasks like Anti-lock Braking System (ABS) that are safety critical in nature, also demand high reliability against transient fault induced soft errors [2] which silently corrupts the results. Existing reliability aware scheduling strategies are offline and real time dynamic scheduling strategies are not reliability aware. This is an initial attempt to bridge this gap and propose reliability aware online scheduling strategies for periodic, aperiodic, and sporadic real time tasks. We also propose a suitable approach to guarantee the maximum load that can be successfully scheduled by the proposed strategies for a heterogeneous architecture.
Somatosensory Evoked Responses Elicited by Haptic Sensations in Midair

The design of human–machine interfaces (HMIs) by virtual haptics is an emerging field of research. So far, the perception of virtual haptic feedback, e.g., generated by focused ultrasound in midair has not been objectively evaluated. This paper demonstrates the feasibility of eliciting somatosensory evoked potentials (SEPs) with ultrasonic stimuli in midair for the first time. The palm was stimulated by short ultrasonic focal points generated by an ultrasound board. The results are compared with a no stimulation condition as well as with the results of a vibro tactile stimulation. The SEPs are analyzed with the wavelet phase synchronization stability (WPSS) and the M-consecutive averaged WPSS. The results indicate a clear SEP waveform elicited by ultrasound. It can be significantly differentiated from a no stimulation condition by the M-consecutive averaged WPSS. These results could enable the possibility of developing an objective evaluation method for virtual haptic feedback in HMIs.
Transition Planning Among US Youth With and Without Special Health Care Needs

Health care transition performance results from the 2016 NSCH are presented for youth with and without SHCNs. BACKGROUND: Researchers have shown that most youth with special health care needs (YSHCN) are not receiving guidance on planning for health care transition. This study examines current transition planning among US youth with and without special health care needs (SHCN). METHODS: The 2016 National Survey of Children’s Health is nationally representative and includes 20 708 youth (12–17 years old). Parents and/or caregivers were asked if transition planning occurred, based on the following elements: (1) doctor or other health care provider (HCP) discussed the eventual shift to an HCP who cares for adults, (2) an HCP actively worked with youth to gain self-care skills or understand changes in health care at age 18, and (3) youth had time alone with an HCP during the last preventive visit. Sociodemographic and health system characteristics were assessed for associations with transition planning. RESULTS: Nationally, 17% of YSHCN and 14% of youth without SHCN met the overall transition measure. Older age (15–17 years) was the only sociodemographic factor associated with meeting the overall transition measure and individual elements for YSHCN and youth without SHCN. Other sociodemographic characteristics associated with transition planning differed among the 2 populations. Receipt of care coordination and a written plan was associated with transition planning for YSHCN. CONCLUSIONS: This study reveals that few youth with and without SHCN receive transition planning support. It underscores the need for HCPs to work with youth independently and in collaboration with parents and/or caregivers throughout adolescence to gain self-care skills and prepare for adult-focused care.
Improving Convolutional Neural Networks Via Compacting Features

Convolutional neural networks (CNNs) have shown great advantages in computer vision fields, and loss functions are of great significance to their gradient descent algorithms. Softmax loss, a combination of cross-entropy loss and Softmax function, is the most commonly used one for CNNs. Hence, it can continuously increase the discernibility of sample features in classification tasks. Intuitively, to promote the discrimination of CNNs, the learned features are desirable when the inter-class separability and intra-class compactness are maximized simultaneously. Since Softmax loss hardly motivates this inter-class separability and intra-class compactness simultaneously and explicitly, we propose a new method to achieve this simultaneous maximization. This method minimizes the distance between features of homogeneous samples along with Softmax loss and thus improves CNNs' performance on vision-related tasks. Experiments on both visual classification and face verification datasets validate the effectiveness and advantages of our method.
Improved Object Detection With Iterative Localization Refinement in Convolutional Neural Networks

To facilitate object localization, the existing convolutional neural network (CNN)-based object detection often requires an object proposal method, which, however, may produce inaccurate region proposals and thus impact the performance. To overcome this setback, this paper presents a novel iterative localization refinement method which, undertaken at a mid-layer of a CNN architecture, progressively refines a subset of region proposals in order to match as much ground-truth as possible. In each iteration, the refinement task is cast into a probabilistic framework based on an ingeniously devised probability function. To expedite the computation of the probability function, a divide-and-conquer paradigm is developed by the theorem of total probability. Moreover, an approximate variant based on a refined sampling strategy is also addressed to further reduce the complexity. The proposed ILR method is not only data-driven and free of learning, but it can also be incorporated with many existing CNN-based object detection algorithms, such as Faster R-CNN to enhance the detection accuracy without changing their configurations. Simulations show that the proposed method can improve the main state-of-the-art works on the PASCAL VOC 2007, 2012 and Youtube-Objects data sets.
Diffusive MIMO Molecular Communications: Channel Estimation, Equalization, and Detection

In diffusion-based communication, for molecular systems, the achievable data rate depends on the stochastic nature of diffusion, which exhibits a severe inter-symbol-interference (ISI). Multiple-input multiple-output (MIMO) multiplexing improves the data rate at the expense of an inter-link interference (ILI). This paper investigates training-based channel estimation schemes for diffusive MIMO (D-MIMO) systems and corresponding equalization methods. Maximum likelihood and least-squares estimators of mean channel are derived, and the training sequence is designed to minimize the mean square error (MSE). The numerical validations in terms of MSE are compared with Cramér–Rao bound derived herein. Equalization is based on decision feedback equalizer (DFE) structure as this is effective in mitigating diffusive ISI/ILI. Zero-forcing, minimum MSE, and least-squares criteria have been paired to DFE, and their performances are evaluated in terms of bit error probability. D-MIMO time interleaving is exploited as an additional countermeasure to mitigate the ILI with remarkable performance improvements. The configuration of nano-transceivers is not static but affected by a Brownian motion. A block-type communication is proposed for D-MIMO channel estimation and equalization, and the corresponding time-varying D-MIMO MC system is numerically evaluated.
A Coded DCSK Modulation System Over Rayleigh Fading Channels

Coded modulation (CM) is a bandwidth efficient framework to approach the capacity limit of the differential chaos shift keying (DCSK) systems. In this paper, we propose an ${M}$ -ary DCSK system operated with CM based on nonbinary protograph low-density parity-check (LDPC) codes over Rayleigh fading channels. First, we investigate the performance of the proposed nonbinary channel coded DCSK (CM-DCSK) and bit-interleaved coded DCSK (BICM-DCSK). In particular, we show that CM-DCSK outperforms BICM-DCSK over Rayleigh fading channels in terms of capacity limit. Compared with BICM-DCSK, CM-DCSK can simplify the receiver structure, since it does not require turbo iteration between the noncoherent detector and channel decoder. Second, guided by the modified extrinsic information transfer (EXIT) analysis, we put forth two new types of nonbinary protograph LDPC codes to approach the capacity limit of CM-DCSK. Both EXIT analysis and simulation results demonstrate that the proposed protograph-coded CM-DCSK achieves a better error performance than BICM-DCSK even with iterative decoding. Furthermore, we show the performance superiority of nonbinary protograph-coded CM-DCSK over a practical ultra-wideband channel. Hence, we conclude that this proposed scheme offers a good alternative for wireless local area network applications.
Output-Feedback Discrete-Time Sliding Mode Control via Disturbance Estimation

In this paper, a novel technique to achieve robustness in sliding mode control for discrete-time systems by measuring only its output is presented. The proposed technique is based on the approximation of an arbitrary discrete-time function by using various of its previous steps. Using this approach, the system disturbance is approximated by a dynamical system and as result, the plant model is extended. Based on the extended model, a state observer is designed to estimate both the system state and the disturbance. Using the observer state, a discrete-time sliding mode controller is then designed to robustly stabilize the perturbed system. The simulation results show the effectiveness of the proposed technique.
Considerations for a Successful Network Service Chain Deployment

Network Functions Virtualization (NFV) along with the Service Chaining paradigm have changed the way that network services are deployed in the underlying network infrastructure. The decoupling of network functions from dedicated middleboxes into software entities has led the Service Providers to offer highly specialized services tailored to the needs of the end users. However, in order to achieve the high performance that the expensive hardware appliances can guarantee, an efficient virtualized network functions (VNF) deployment should be performed. This paper proposes a novel VNF placement approach with the goal to minimize the deployment cost and maximize the achieved throughput. Results reveal the need of considering the throughput degradation when consolidating multiple VNFs on the same server in order to increase the overall performance.
Fast Oov Words Incorporation Using Structured Word Embeddings for Neural Network Language Model

Recently, deep learning approaches have been widely used in language modeling and achieved great success. However, the out-of-vocabulary (OOV) words are often estimated in a rather crude way using only one special symbol, which ignores the linguistic information. In this paper we present an LSTM language model with structured word embeddings to tackle this problem. In our model, both input and output embeddings of LSTM language model are deployed with structured word embeddings. Utilizing syntactic-level and morphological-level parameters sharing, OOV words can be incorporated into the proposed model without retraining. The LSTM language model with structured word embeddings is instantiated for Chinese. Experiments show that the proposed model achieves PPL improvement on OOV words, and can be further integrated into automatic speech recognition systems for fast vocabulary updating.
Recursive Evaluation of Sure for Total Variation Denoising

Recently, total variation (TV)-based regularization has become a standard technique for signal denoising. The reconstruction quality is generally sensitive to the value of regularization parameter. In this work, based on Cham-bolle's algorithm, we develop two data-driven optimization schemes based on minimization of Stein's unbiased risk estimate (SURE)-statistically equivalent to mean squared error (MSE). First, we propose a recursive evaluation of SURE to monitor the estimation error during Chambolle's iteration; the optimal value is then identified by the minimum SURE. Second, for fast optimization, we perform alternating update between regularization parameter and solution within Chambolle's iteration. We exemplify the proposed methods with both 1-D and 2-D signal denoising. Numerical experiments show that the proposed methods lead to highly accurate estimate of regularization parameter and nearly optimal denoising performance.