Miracle-dz/UniFAR-FTUs huggingface dataset:
End-To-End Low-Resource Lip-Reading with Maxout Cnn and Lstm

Lip-reading is the task of recognizing speech solely from the visual movement of the mouth. Although recent works have demonstrated the effectiveness of convolutional neural network (CNN) and long short-term memory (LSTM) recurrent neural network in lip-reading, similar architectures under low-resource scenario have not yet been explored. Our proposed end - to-end deep learning model fuses conventional CNN and bidirectional LSTM (BLSTM) together with max-out activation units (maxout-CNN-BLSTM), and is capable of attaining a word accuracy of 87.6% on the Ouluvs2 corpus, offering an absolute improvement of 3.1 % to the previous state-of-the-art auto-encoder-BLSTM model. To the best of our knowledge, this is the first end - to-end low -resource lip-reading system that does not require any separate feature extraction stage nor pre-training phase with external data resources. This is also the first work that utilizes maxout units in both CNN and LSTM in one single deep neural network.
GMM-Based Iterative Entropy Coding for Spectral Envelopes of Speech and Audio

Spectral envelope modelling is a central part of speech and audio codecs and is traditionally based on either vector quantization or scalar quantization followed by entropy coding. To bridge the coding performance of vector quantization with the low complexity of the scalar case, we propose an iterative approach for entropy coding the spectral envelope parameters. For each parameter, a univariate probability distribution is derived from a Gaussian mixture model of the joint distribution and the previously quantized parameters used as a-priori information. Parameters are then iteratively and individually scalar quantized and entropy coded. Unlike vector quantization, the complexity of proposed method does not increase exponentially with dimension and bitrate. Moreover, the coding resolution and dimension can be adaptively modified without retraining the model. Experimental results show that these important advantages do not impair coding efficiency compared to a state-of-art vector quantization scheme.
A Study of Noise PSD Estimators for Single Channel Speech Enhancement

The estimation of the noise power spectral density (PSD) forms a critical component of several existing single channel speech enhancement systems. In this paper, we evaluate one new and some of the existing and commonly used noise PSD estimation algorithms in terms of the spectral estimation accuracy and the enhancement performance for different commonly encountered background noises, which are stationary and non-stationary in nature. The evaluated algorithms include the Minimum Statistics, MMSE, IMCRA methods and a new model-based method.
Embroidered Electrodes for Control of Affordable Myoelectric Prostheses

The low-cost manufacturing and maintenance of prostheses is of vital importance to their successful deployment in developing countries. Low-cost prosthesis actuation is generally achieved by combining pre-programmed control strategies, with surface-electromyographic measurements taken from the residual limb. In a standard setting, these signals are measured with disposable gel electrodes. However, this limit on electrode reuse requires that prosthesis users have a stable supply of electrodes. Alternatively, the textile electrodes sewn from conductive thread are studied in the context of hand gesture recognition to consider their future use with low-cost prostheses. In this paper, it is demonstrated that textile electrodes can be applied for gesture recognition. To do so, surface electromyography (sEMG) experiments are run in South Africa on three amputees where they were asked to perform gestures with their phantom limb (i.e., the missing limb segment). A gesture recognition method is implemented, and the classification accuracy with data recorded from textile electrodes is compared to that from gel electrodes. Further analysis examining the relationship between classifier performance and physiological parameters are performed. Results show that textile electrodes can be used to perform accurate gesture recognition, and are comparable to disposable gel electrodes. This demonstrates that low-cost sensory systems are not barrier to myoelectric control in developing countries.
Negotiating with a Robot: Analysis of Regulatory Focus Behavior

Companion robots are more and more taking the role of caregivers for elderly people. Elderly people sometimes take the advice given by their family members or caregivers as a criticism. In this context, persuasive communication skills could be helpful. A social psychology theory called Regulatory Focus states that people have one of two inclinations when taking decisions: Promotion or Prevention Focus. Also, based on these inclinations, people can be influenced by the way the message is sent, including the speed of the speech and the amplitude of body gestures. In this paper, we analyze the influence of Regulatory Focus on a negotiation scenario, using 3 conditions: (1) a robot with a promotion behavior, (2) a robot with a prevention behavior, and (3) a robot with a neutral behavior. Our results support the results found in the psychology literature related to Regulatory Focus, suggesting that Promotion participants were more influenced by the robot showing a Promotion based behavior. Moreover, Prevention participants were more relaxed on the condition with the robot showing a Prevention based behavior, and accepted the biggest concession between the initial and final offer.
Evaluating Models of Dynamic Functional Connectivity Using Predictive Classification Accuracy

Dynamic functional connectivity has become a prominent approach for tracking the changes of macroscale statistical dependencies between regions in the brain. Effective parametrization of these statistical dependencies, referred to as brain states, is however still an open problem. We investigate different emission models in the hidden Markov model framework, each representing certain assumptions about dynamic changes in the brain. We evaluate each model by how well they can discriminate between schizophrenic patients and healthy controls based on a group independent component analysis of resting-state functional magnetic resonance imaging data. We find that simple emission models without full covariance matrices can achieve similar classification results as the models with more parameters. This raises questions about the predictability of dynamic functional connectivity in comparison to simpler dynamic features when used as biomarkers. However, we must stress that there is a distinction between characterization and classification, which has to be investigated further.
ReasoNet: Inferring Network Policies Using Ontologies

Modern Software Defined Networking (SDN) control stacks consist of multiple abstraction and virtualization layers to enable flexibility in the development of new control features. Rich data modeling frameworks are essential when sharing information across control layers. Unfortunately, existing Network Operating System (NOS) data modeling capabilities are limited to simple type-checking and code templating. We present an exploration of a more extreme point on SDN data modeling: ReasoNet. Developers can use semantic web technologies to enrich their data models with reasoning rules and integrity/consistency constraints, and automate state inference across layers. We demonstrate the ability of ReasoNet to automate state verification and cross-layer debugging, through the implementation of two popular control applications, a learning switch and a Quality of Service (QoS) policy engine.
Achieving Resilience in SDN-Based Smart Grid: A Multi-Armed Bandit Approach

With the advancement in electrical power systems, control engineering, and information and communication technologies (ICT), remarkable efforts have been made to improve the current electrical power grid, resulting in the so-called intelligent grid or the smart grid (SG). The envisioned smart grid heavily relies on ICT to support two-way communication, demand side management, and other critical smart grid operations. In case of an attack in the SG environment, communication link failures may occur. Moreover, an attacker may also compromise the switch within the SG communication network, thus leading to the breakdown of several communication links. Consequently, important information will not be communicated to and from the SG entities like relays, and remote terminal units (RTUs), As a result, cascading failures or blackouts may occur. Identification of these link failures and choosing alternative communication paths at run-time is indispensable. Thus, to deal with this issue, we consider an SDN-based smart grid setting in which the SDN controller in coordination with the network switches, is capable of learning about the link failures inside the network and then adapt to the changing network conditions dynamically. We model this problem as Multi-armed bandit problem and propose a link failure learning (LFL-MAB) algorithm. The proposed algorithm has the capability to learn the strategy adopted by the attacker and select those communication links which are reliable. Simulation results show that the proposed algorithm outperform other approaches and ensure reliable communication and resiliency within the smart grid network.
Coupled Experimental and Computational Approach for CABRI Power Transients Analysis

CABRI is an experimental pulse reactor, funded by the French Nuclear Safety and Radioprotection Institute and operated by Commissariat à l’Énergie Atomique et aux Énergies Alternatives at the Cadarache Research Center. It is designed to study fuel behavior under reactivity-initiated accident conditions. In order to produce the power transients, reactivity is injected by depressurization of a neutron absorber (3He) situated in the so-called transient rods inside the reactor core. The CABRI reactivity injection system allows us to generate structured transients based on specific sequences of depressurization. For such transients, the time difference between the openings of two valves of the reactivity injection system has an important impact on the power pulse shape. A kinetic point code, SPARTE, was developed in order to replace the older DULCINEE code dedicated to the modeling and prediction of CABRI power transients. The SPARTE code includes new models of 3He depressurization based on CFD calculations, variable Doppler coefficient based on Monte Carlo calculations, and variable axial neutron flux profile. The density and Doppler models have a large impact on power transient prediction. For low initial pressure transients, the major uncertainty comes from the reactivity injected by the 3He depressurization. For high initial pressure transients, the 3He heating during the power pulse (“TOP effect”) is responsible of an additional injection of reactivity that needs to be modeled precisely.
A Syntactic Content-Based Recommender Based on combination of ACO and GA in large scholarly data

The quantity of research papers or literature has been increasing continually which origins the explosion of information. The outcome of this fast data growth is large options provided to internet users when searching for a paper, product or service. These will results difficulty in building decisions to internet users. Thus, this generates need to select, order and efficiently deliver important information to mend the problem of information burden. Recommenders are crucial systems to avoid the information burden, by filtering the huge group of information and suggesting relevant data to the operator. Recommender schemes solve this matter via inspecting through large dimensions of generated dynamic information to supply users personalized matter. Plenty of recommendation systems are available with different accuracies. Enhancing the worth of recommendations is the most important dares for recommendation schemes. This paper offers an innovative recommender system termed a Syntactic Recommender which deploys an upgraded feature selection system that is combination of ACO-GA algorithms to raise the accuracy of recommendations in recommender system. This paper emphases on two fundamental concepts, first is performing and giving more exact object predictions to the operator and second is handling huge volume of data. The aim of this paper is offering recommendation outcomes based on user's request with accuracy and competency. We also realized that the outcomes of recommendation systems are correct.
A Captcha Design Based on Visual Reasoning

CAPTCHA is a reverse Turing test to distinguish humans from machines. It is widely used in the internet industry for cyber security. A good CAPTCHA is supposed to be easy for humans but difficult for machines. Many existing CAPTCHA implementations leverage the inability of automatic visual recognition, e.g., recognizing the text or other objects in an image. These CAPTCHAs are becoming more and more vulnerable recently, due to the rapid development of visual recognition techniques. This paper presents our study of using visual reasoning in CAPTCHA design. This CAPTCHA asks the users to find specific object(s) in an image according to a given text query. It is generally easy for humans to understand the text query and make sophisticated reasoning about the image, but still remains difficult and computationally expensive for machines. We describe the CAPTCHA design, provide usability analysis and present security experiments. Moreover, we show that the security can be further improved by the use of neural style transfer.
Automated Non-Invasive Measurement of Sperm Motility and Morphology Parameters

Measuring the motility and morphology parameters of motile cells is important for revealing their functional characteristics. This paper presents automation techniques that, for the first time, enable automated, non-invasive measurement of motility and morphology parameters of individual sperms. Compared to the status quo of qualitative estimation of single sperm's motility and morphology based on embryologists' empirical experience, the automation techniques provide quantitative data in nearly real time. An adapted joint probabilistic data association filter (JPDAF) was used for multi-sperm tracking and tackled challenges of identifying sperms that intersect or have small spatial distances. Since the standard differential interference contrast (DIC) imaging method has side illumination effect which causes inherent inhomogeneous image intensity and poses difficulties for accurate sperm morphology measurement, we integrated total variation norm into the quadratic cost function method, which together effectively removed inhomogeneous image intensity and retained sperm's subcellular structures after DIC image reconstruction. In order to relocate the same sperm of interest identified under low magnification after switching to high magnification, coordinate transformation was conducted to handle the changes in the field of view caused by magnification switch. Experimental results demonstrated an accuracy of 95.6% in sperm motility measurement and errors <10% in morphology measurement.
Orchestration of Microservices for IoT Using Docker and Edge Computing

The world of connected devices has led to the rise of the Internet of Things paradigm, where applications rely on multiple devices, gathering and sharing data across highly heterogeneous networks. The variety of possible mechanisms, protocols, and hardware has become a hindrance in the development of architectures capable of addressing the most common IoT use cases, while abstracting services from the underlying communication subsystem. Moreover, the world is moving toward new strict requirements in terms of timeliness and low latency in combination with ultra-high availability and reliability. Thus, future IoT architectures will also have to support the requirements of these cyber-physical applications. In this regard, edge computing has been presented as one of the most promising solutions, relying on the cooperation of nodes by moving services directly to end devices and caching information locally. Therefore, in this article, we propose a modular and scalable architecture based on lightweight virtualization. The provided modularity, combined with the orchestration supplied by Docker, simplifies management and enables distributed deployments, creating a highly dynamic system. Moreover, characteristics such as fault tolerance and system availability are achieved by distributing the application logic across different layers, where failures of devices and micro-services can be masked by this natively redundant architecture, with minimal impact on the overall system performance. Experimental results have validated the implementation of the proposed architecture for on-demand services deployment across different architecture layers.
Using Optimal Mass Transport for Tracking and Interpolation of Toeplitz Covariance Matrices

In this work, we propose a novel method for interpolation and extrapolation of Toeplitz structured covariance matrices. By considering a spectral representation of Toeplitz matrices, we use an optimal mass transport problem in the spectral domain in order to define a notion of distance between such matrices. The obtained optimal transport plan naturally induces a way of interpolating, as well as extrapolating, Toeplitz matrices. The constructed covariance matrix interpolants and ex-trapolants preserve the Toeplitz structure, as well as the positive semi-definiteness and the zeroth covariance of the original matrices. We demonstrate the proposed method's ability to model locally linear shifts of spectral power for slowly varying stochastic processes, illustrating the achievable performance using a simple tracking problem.
LineDrone Technology: Landing an Unmanned Aerial Vehicle on a Power Line

This paper presents the design of a multirotor unmanned aerial vehicle (UAV) capable of landing semiautomatically on a power line while carrying a payload. The vehicle then rolls along the line to perform an inspection. Special attention is given to the vehicle's onboard vision system, which consists of a monocular camera and LiDAR used together to compute the pose of the vehicle relative to the power line. Landing assistance is provided to the pilot by a position-based visual controller that aligns and keeps the vehicle centered along the power line. The pilot remains in control of vertical and longitudinal movement during descent. The proposed approach was tested on a full-scale test line and shows promise for future applications of high value to the electric industry such as non-destructive testing of power transmission lines.
Cramér-Rao Bound for Line Constrained Trajectory Tracking

In this paper, target tracking constrained to short-term linear trajectories is explored. The problem is viewed as an extension of the matrix decomposition problem into low-rank and sparse components by incorporating an additional line constraint. The Cramér-Rao Bound (CRB) for the trajectory estimation is derived; numerical results show that an alternating algorithm which estimates the various components of the trajectory image is near optimal due to proximity to the computed CRB. In addition to the theoretical contribution of incorporating an additional constraint in the estimation problem, the alternating algorithm is applied to real video data and shown to be effective in estimating the trajectory despite it not being exactly linear.
Event-triggered stabilization over digital channels of linear systems with disturbances

In the same way that subsequent pauses in spoken language are used to convey information, it is also possible to transmit information in communication systems not only by message content (data payload), but also with its timing. This paper presents an event-triggering strategy that utilizes timing information by transmitting in a state-dependent fashion. We consider the stabilization of a continuous-time, time-invariant, linear plant over a digital communication channel with bounded delay and in the presence of bounded plant disturbances. We propose an encoding-decoding scheme that guarantees a sufficient information transmission rate for stabilization of the plant. In addition, we determine a lower bound on the information transmission rate of the sensor which is necessary for stabilization. We show that for small values of the delay, the timing information implicit in the triggering events is enough to stabilize the plant with any positive information transmission rate. In contrast, when the delay increases beyond a critical threshold, the timing information alone is not enough to stabilize the plant and the data payload transmission rate begins to increase. Finally, large values of the delay require transmission rates higher than what prescribed by the classic data-rate theorem. Our results also provide a novel encoding-decoding scheme for complex systems, which can be readily applied to diagonalizable multivariate systems with complex eigenvalues. The identified rates depend on parameters in the event-triggered law and plant dynamics, and the bounds on the plant disturbances and channel delays. The technical treatment does not rely upon any a priori probabilistic distribution of plant disturbances, initial condition, or delay. Our theoretical results are also validated by numerical simulation.