Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Sequential Characteristics in Speaker Bottleneck Feature for Text-Dependent Speaker Verification

In this paper, given the speaker bottleneck feature vectors extracted with speaker discriminant neural networks, we focus on using the sequential speaker characteristics for text-dependent speaker verification. In each evaluation trial, speaker supervectors are used as the representations of the sequential speaker characteristics rendered in the compared speech utterances. To this end, dynamic time warping is used to warp the variable-length speaker feature vector sequences of the utterances to the same length. Thereafter for every utterance, a speaker supervector can be obtained as the concatenation of its speaker feature vectors. We use Euclidean distance and support vector machine (SVM) to compute the decision score on the speaker supervectors. Our experiments on a Microsoft internal keyword-spotting database showed the effectiveness of the proposed speaker supervector for text-dependent speaker verification. Moreover, when SVM backend was used in scoring, the speaker supervector achieved the best EER performance 1.627%, better than the combination of i-vector and probabilistic linear discriminant analysis.
GOMSF: Graph-Optimization Based Multi-Sensor Fusion for robust UAV Pose estimation

Achieving accurate, high-rate pose estimates from proprioceptive and/or exteroceptive measurements is the first step in the development of navigation algorithms for agile mobile robots such as Unmanned Aerial Vehicles (UAVs). In this paper, we propose a decoupled Graph-Optimization based Multi-Sensor Fusion approach (GOMSF) that combines generic 6 Degree-of-Freedom (DoF) visual-inertial odometry poses and 3 DoF globally referenced positions to infer the global 6 DoF pose of the robot in real-time. Our approach casts the fusion as a real-time alignment problem between the local base frame of the visual-inertial odometry and the global base frame. The alignment transformation that relates these coordinate systems is continuously updated by optimizing a sliding window pose graph containing the most recent robot's states. We evaluate the presented pose estimation method on both simulated data and large outdoor experiments using a small UAV that is capable to run our system onboard. Results are compared against different state-of-the-art sensor fusion frameworks, revealing that the proposed approach is substantially more accurate than other decoupled fusion strategies. We also demonstrate comparable results in relation with a finely tuned Extended Kalman Filter that fuses visual, inertial and GPS measurements in a coupled way and show that our approach is generic enough to deal with different input sources in a straightforward manner. Video – https//youtu.be/GIZNSZ2soL8
New therapies to relieve pain

EMBO Reports (2018) e46925 Opioids are a largely efficient medication for relieving severe pain and are prescribed for a wide variety of indications including metastatic cancer, arthritis, neuropathy or post‐surgical pain. These drugs target opioid receptors to produce effects similar to morphine and include moderately strong painkillers such as oxycodone and hydrocodone (Vicodin), and strong drugs like fentanyl, which resembles opium‐derived morphine and heroin. The great efficiency of opioids in treating severe pain has also been the major drawback for longer‐term use given their addictive power. This does not matter for short‐term use, for instance to ease post‐operative pain, or terminal cancer when the primary requirement is to relieve suffering. Yet, the addictive side effect has become a huge societal problem; in addition, opioids do not work against all types of chronic pain, including some of the most severe cases such as cancer‐induced bone pain or some cases of neuropathy resulting from nerve damage. These problems have spurred efforts to develop new drugs for treating pain with some success in developing derivatives with reduced side effects. The other major avenue of research is the identification of relevant pain pathways or protein binding sites including those involved in the placebo effect. Their efficiency has spurred physicians to prescribe opioids too readily, while drug companies have heavily advertised their effects and understated the side effects to boost usage. This three‐way collusion between patients, doctors and pharmaceutical companies has stoked the ongoing epidemic of opioid addiction that has afflicted the USA and Canada in particular, but also other developed countries in Europe and Asia. Apart from exacting a heavy social toll through loss of motivation or loss of jobs, impact on relationships, crime and so on, opioid addiction has dangerous physiological effects, notably on the respiratory system, which causes most of the overdose …
Review on Automatic Music Transcription System

In this paper, the literature survey of the automatic music transcription system have been presented. Now a day's most of the research work going on Music transcription and it is considered to be a most difficult problem even by human experts and current music transcription systems fail to match human performance. As compare to Monophonic AMT the Polyphonic AMT is a difficult problem because in polyphonic concurrently sounding notes from one or more instruments cause a complex interaction and overlap of harmonics in the acoustic signal. So we concentrate on all methods of polyphonic AMT. Most of the music transcription systems were developed for the instruments typically used in western music like Piano, Guitar etc. but very less paper/work has been publishing in the domain of harmonium note transcription which is widely used the instrument in Indian musical concerts.
The genetics of phenotypic plasticity. XVI. Interactions among traits and the flow of information

Although the environment varies, adaptive trait plasticity is uncommon, which can be due to either costs or limitations. Currently there is little evidence for costs of plasticity; limitations are a more promising explanation, including information reliability. A possible cause for a decrease in information reliability is the channeling of environmental information through one trait that then affects the phenotype of a second trait, the information path. Using an individual‐based simulation model, I explored the ways in which configurations of trait interactions and patterns of environmental variation in space and time affect the evolution of phenotypic plasticity. I found that genotypes and phenotypes evolved to shorten and simplify the information path from the environment to fitness. A shortened path was characterized by a decrease in the amount of plasticity for traits that had a less direct connection between the environment of development and fitness. A simplified path was characterized by a decrease in the amount of plasticity for traits that had multiple paths between the environment and their phenotype. These results suggest that an eighth proposition be added to the theory of the evolution of phenotypic plasticity: Trait plasticity will evolve to minimize the information path between the environment and fitness.
Subset Selection for Kernel-Based Signal Reconstruction

In this work, we introduce subset selection strategies for signal reconstruction based on kernel methods, particularly for the case of kernel-ridge regression. Typically, these methods are employed for exploiting known prior information about the structure of the signal of interest. We use the mean squared error and a scalar function of the covariance matrix of the kernel regressors to establish metrics for the subset selection problem. Despite the NP-hard nature of the problem, we introduce efficient algorithms for finding approximate solutions for the proposed metrics. Finally, numerical experiments demonstrate the applicability of the proposed strategies.
Optimal Solution Generation from Reviews and Micro-Reviews using Greedy Algorithm

The survey matter that is available, and the truth of reviews being exceedingly different and needlessly made of more words, clients habitually encounter the issue of choosing the suitable reviews on devour. Micro-reviews are rising similarly as another kind of web survey substance in the online networking. Micro-reviews are presented by clients in the form check-in benefits. They are brief and apt (about 200 characters long) and are profoundly concentrated, as opposed to what the long and wordy reviews recommend. Here, in this paper, a new problem for review mining is proposed, that obtains together these two different sources of review content. Specifically, the scope of micro-reviews concerning a particular destination is utilized for choosing a set of reviews that disguise proficiently those remarkable viewpoints for a substance or item. The approach comprises of a two-stage procedure: matching audit penalties to micro-reviews, what's more like picking a small set of reviews which disguise as a number of micro-reviews concerning the likely illustration, with couple penalties. The goal is defined similar to a combinatorial streamlining problem, and hint at how to infer an ideal result utilizing basic straight customizing. We additionally recommend a proficient heuristic calculation that approximates the optimal results. Finally, contributing an approach for implementing a system which determines query facets by combining frequent lists from the top results.
Structured Prediction of Dense Maps between Geometric Domains

We introduce a new framework for learning dense correspondence between deformable geometric domains such as polygonal meshes and point clouds. Existing learning based approaches model correspondence as a labelling problem, where each point of a query domain receives a label identifying a point on some reference domain; the correspondence is then constructed a posteriori by composing the label predictions of two input geometries. We propose a paradigm shift and design a structured prediction model in the space of functional maps, linear operators that provide a compact representation of the correspondence. We model the learning process via a deep residual network which takes dense descriptor fields as input, and outputs a soft map between the two given objects. The resulting correspondence is shown to be accurate on several challenging shape correspondence benchmarks.
Stabilization of Uncertain Nonlinear Systems via Immersion and Invariance based Sliding Mode Control

This paper introduces a new methodology for stabilization of uncertain nonlinear system using immersion and invariance (I&I) based sliding mode control. The attractivity of the manifold ensures the reflection of the desired system behavior by I&I principle for the nominal system. Higher-order sliding mode of an arbitrary $r^{th}$ order is invoked to replace the stabilizing control law of an output regulator theory to derive the off-the-manifold with completely controlling the disturbance/uncertainties in finite-time. This makes the system more systematic to achieve the equilibrium point asymptotically. Finally, the validation of proposed strategy is tested on a magnetic-levitation system.
A k-NN Query Method Over Encrypted Data

In this paper, we mainly study the problem of computing the k nearest neighbor over the encrypted data. We solve this question from two aspects. Firstly, we focus on the query efficiency. It is necessary to improve the user experience and save computing resources through reducing query time. We propose a k nearest neighbor query algorithm based on hierarchical Clustering. This algorithm uses the pruning rules to exclude most of the non-nearest neighbor data and improve query efficiency. Secondly, considered from data's security, we use the SSED algorithm which can safely compute distance between two encrypted data by using the properties of homomorphic encryption algorithm. Then the SSED algorithm is combined with the hierarchical clustering algorithm to realize the computation of k nearest neighbors over the ciphertext. The experiments show that the algorithm proposed in this paper has high query efficiency.
Symplectic invariants for parabolic orbits and cusp singularities of integrable systems

We discuss normal forms and symplectic invariants of parabolic orbits and cuspidal tori in integrable Hamiltonian systems with two degrees of freedom. Such singularities appear in many integrable systems in geometry and mathematical physics and can be considered as the simplest example of degenerate singularities. We also suggest some new techniques which apparently can be used for studying symplectic invariants of degenerate singularities of more general type. This article is part of the theme issue ‘Finite dimensional integrable systems: new trends and methods’.
Dynamic Modeling and Identification of an Heterogeneously Actuated Underwater Manipulator Arm

This paper deals with the dynamic modeling and identification of an electrically driven underwater robot manipulator. The proposed study includes the dynamic modeling of the actuators of the arm as well as the identification of the parameters of the model. The proposed method deals with the specific case of heterogeneously actuated arms, namely arms with actuators behaving differently for each joint, being considered at the kinematic level. Indeed, we show how to estimate the arms parameters when some of their revolute joints are directly actuated by geared motors, while the others are actuated by linear actuators. A minimum set of identifiable parameters is determined, and adequate excitation trajectories are generated and used in the identification procedure. Realtime experimental validation on the manipulator arms of Ifremer's HROV (Hybrid Remotely Operated Vehicle) Ariane underwater vehicle demonstrates that the proposed method improves the estimation of the dynamic model.
Implementing Fact-Checking in Journalistic Articles Shared on Social Media in the Philippines Using Knowledge Graphs

In the technology age, articles with fraudulent content are rampant, especially articles shared on social media. Misinformation could just be an inaccuracy at its best, or it could lead to normalizing false information at worst. To aid the predicament, the researchers created a system that will “fact check” suspicious articles against those articles that have been deemed credible, reliable, and more accurate, in order to help fight deceiving content that may be detrimental to society. The journal regarding computational fact checking that was published by Ciampaglia, et. al. (2015) from the Indiana University in the USA entitled Computational Fact Checking from Knowledge Networks, was used as the basis and inspiration for this thesis. The researchers made use of the undirected graph (UG) together with a part-of-speech (POS) tagging algorithm to create a knowledge graph (KG) that would serve as the center of the system. Five different POS tagging algorithms were paired with the UG to assess which combination would yield the best results, these are Conditional Random Fields, Logistic Regression, a Hybrid of CRF and LR, Random Forests, and K-Nearest Neighbors. Random Forests and K-Nearest Neighbors were classification algorithms used in Ciampaglia's study. It was concluded that among the 5 pairs of UG and POS Tagging algorithms, the Hybrid of CRF and LR used as a POS tagger, together with the UG, created the most efficient KG.
A Graph Based Document Retrieval Method

A new document retrieval method based on graph was proposed in this paper. Queries and documents are represented by graphs. The paper also proposes the concept of the document semantic unit in consideration of the overhead of graph computing. The size of semantic unit is used as the granularity for graph construction. This new method puts queries and documents in an unequal level instead of regarding them as equivalent entities which conventional IR system does. The paper further proposes the similarity calculating method of graphs based on general maximum common subgraph. The result of the experiment shows this method is able to yield better document retrieval results.
Automatic Evaluation and Predictive Analysis of Optic Nerve Head for the Detection of Glaucoma

The automatic retinal image analysis is emerging as one of the most important primary screening tools for early detection and treatment for eye diseases. Glaucoma is a severe human eye disease leading to the permanent loss of vision. The manual examination of optic nerve head of the retina using Color Fundus Imaging (CFI) is a standard procedure used for predicting glaucoma, but the detection and diagnosis varies widely across different ophthalmologists. In this paper we present an automatic technique for the segmentation of the cup region from the optical disc(OD) region in RGB channels to calculate the parameters used for the predictive analysis of glaucoma. The accurate segmentation of the cup region and the optical disc from the retinal fundus image serves as an important step for the calculation of cup-to-disc area ratio (ACDR). The quantitative evaluation of this predictive analysis is done on a freely available database with a result accuracy of 83.168%.
Insight into the Self-Assembling Properties of Peptergents: A Molecular Dynamics Simulation Study

By manipulating the various physicochemical properties of amino acids, the design of peptides with specific self-assembling properties has been emerging for more than a decade. In this context, short peptides possessing detergent properties (so-called “peptergents”) have been developed to self-assemble into well-ordered nanostructures that can stabilize membrane proteins for crystallization. In this study, the peptide with “peptergency” properties, called ADA8 and extensively described by Tao et al., is studied by molecular dynamic simulations for its self-assembling properties in different conditions. In water, it spontaneously forms beta sheets with a β barrel-like structure. We next simulated the interaction of this peptide with a membrane protein, the bacteriorhodopsin, in the presence or absence of a micelle of dodecylphosphocholine. According to the literature, the peptergent ADA8 is thought to generate a belt of β structures around the hydrophobic helical domain that could help stabilize purified membrane proteins. Molecular dynamic simulations are here used to image this mechanism and provide further molecular details for the replacement of detergent molecules around the protein. In addition, we generalized this behavior by designing an amphipathic peptide with beta propensity, which was called ABZ12. Both peptides are able to surround the membrane protein and displace surfactant molecules. To our best knowledge, this is the first molecular mechanism proposed for “peptergency”.
Path Tracking of a Two-Wheel Steering Mobile Robot: An Accurate and Robust Multi-Model Off-Road Steering Strategy

In this paper, the problem associated with accurate control of a two-wheel steering mobile robot following a path is addressed thanks to a backstepping control strategy. This approach involves an observer to estimate the grip conditions, based on previous work, and the proposed control algorithm for the front axle. Since the significant parameters of the grip conditions are available from the observer, namely the sideslip angles and the cornering stiffnesses, it is then suitable to include them into an algorithm to control mobile robots and obtain a more accurate path tracking. This is made possible by gathering into a single backstepping approach both kinematic and dynamic models. This new point of view permits to take account of both kinematic and dynamic behaviors and grip parameters in the control law. The proposed approach is experimentally evaluated at different speeds and compared with two other state-of-the-art path tracking algorithms and evaluated for several values of lateral deviations.
Decentralized Control for Large-Scale Nonlinear Systems With Unknown Mismatched Interconnections via Policy Iteration

In this paper, the decentralized control problem is solved based on a policy iteration algorithm for large-scale nonlinear systems with unknown mismatched interconnections. The unknown interconnection is approximated by a neural network with local states of isolated subsystem and substituted reference states of coupled subsystems. Then, an adaptive estimation term is utilized to construct the improved local performance index function that reflects the substitution error. Hereafter, the closed-loop large-scale nonlinear system is guaranteed to be ultimately uniformly bounded by the implementation of a set of developed decentralized optimal control policies. Two simulation examples are given to verify the effectiveness of the presented scheme. The significant contribution of this scheme lies in that it removes the common assumptions on satisfying matching condition and upper boundedness of interconnections, when designing the decentralized optimal control for large-scale nonlinear systems.
Extern Objects in P4: an ROHC Header Compression Scheme Case Study

P4 is an emergent packet-processing language with which the user can describe how the packets are to be processed in a switching element. This paper presents a way to implement complex operations that are not natively supported in P4. In this work, we explored two different methods to add extensions to P4: i) using new native primitives and ii) using extern instances. As a case study, an ROHC entity was implemented and invoked in a P4 program. The tests showed similar relative performance in both methods in terms of normalized packet latency. However, extern instances appear to be more suitable for target-specific switching applications, where the manufacturer/vendor can specify its own specific operations without changes in the P4 syntax and semantics. Extern instances only require changes in the target-specific backend compiler while keeping the P4 frontend compiler unchanged. The use of externs also results in a more elegant code solution since they are implemented outside the switch-core, thus reducing side effects risks that can be caused by a modification in a switch pipeline implementation.
A New Approach for Automatic Face Emotion Recognition and Classification Based on Deep Networks

Recognition of human emotions has been a challenging topic in field of human-computer interaction. In order that there is more natural interaction between human and computer, the computer must be able to recognize, distinguish and respond to human emotions. Also Automated Face Expression Recognition (FER) is still continuing to be a challenging and concerning problem in Computer Vision. In spite of all the efforts being made in the evolution of various methods for FER, the present methods lack popularity when it comes to unseen images or pictures captured in wild settings. This paper tends to design an artificially intelligent system capable of emotion recognition through facial expressions of unknown people. The network in this paper consists of three convolutional layers each followed by max pooling and ReLU. The network is trained on FER2013 dataset and tested on RaFD dataset thus giving a wide range of training images to the network, so that it can overcome the basic problem of recognition of unknown faces. The pertinence of the final model is depicted in a live video application that can instantaneously return users emotions based on their facial posture. The accuracy obtained by this method was 68%, which is better than the previous state-of-the-arts methods. The results provide an important insight on the significance of using different datasets for training and validation.