Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Socio-Affective Information in Physical Perception Aimed to Telepresence Robots

The aim of telepresence robotics is to provide the users with ubiquitous social immersion. It implies giving the teleoperators a proprioceptive sense of the social signals they exchange with their remote interlocutors. In particular, they need the sound volume of the telepresence robot to be adjust so it is appropriate to the acoustic conditions in the remote space. However, this intensity adjustment could conflict with the socio-affects expressed by the teleoperator and makes them unrecognizable or inconsistent with the behaviour of the telepresence robot. It would be therefore interesting to control not only voice intensity, but the “social value” of the vocal signal, so the eventual artefacts of telecommunication could be counterbalanced. To this end, we propose an experiment for measuring, analyzing and modeling how people perceive their interlocutor spatially, according to proxemics and socio-affective variations. The used methodology is based on a “hidden camera” scenario to achieve ecological measures of human acoustic perception. Contrary to our primary hypothesis, we could not find any correlation between the perceived distance and expressed socio-affect because of a great inter-subjects variability. However, there is a clear effect of socio-affect and intensity on the perception of orientation.
Car Popularity Prediction: A Machine Learning Approach

Today is a world of technology with a foreseen future of a machine reacting and thinking same as human. In this process of emerging Artificial Intelligence, Machine Learning, Knowledge Engineering, Deep Learning plays an essential role. In this paper, the problem is identified as regression or classification problem and here we have solved a real world problem of popularity prediction of a car company using machine learning approaches.
Towards Data Governance in the Internet of Things

The diffusion of Internet of Things (IoT) technologies enables the provision of advanced and valuable services, but also raises several challenges. First of all, the increasing number of heterogeneous interconnected devices creates scalability and interoperability issues and thus a ﬂexible middleware platform is needed to manage all the sources together with all the tasks related to data collection and integration. In fact, the large amount of data has to be properly managed. In particular, on the one hand, data have to be protected from security threats; on the other hand, it is necessary to consider that data are useful only if their quality is suitable for the processes in which they have to be used. For these reasons, it is important that applications/users that aim to exploit the collected data are aware of data quality and security levels in order to understand if data can be trusted and thus used. In this chapter, we present a distributed architecture for managing IoT data extraction and processing that also includes algorithms for the assessment of data quality and security levels of considered sources. A prototype of such an architecture has been realised: through a user interface it is possible to access data services able to ﬁlter data from IoT devices on the basis of security and data quality requirements.
Group Sparse Representation With WaveNet Vocoder Adaptation for Spectrum and Prosody Conversion

The statistical approach to voice conversion typically consists of a feature conversion module followed by a vocoder. So far, the feature conversion studies are mainly focused on the conversion of spectrum. However, speaker identity is also characterized by prosodic features, such as fundamental frequency (F0) and energy contour among others. In this paper, we study the transformation of speaker characteristics both in terms of spectrum and prosody. We propose two novel techniques that effectively use a limited amount of source-target training data and leverage a large general speech corpus to improve the voice conversion quality. First, we study the phonetic sparse representation under the group sparsity mathematical formulation. We use phonetic posteriorgrams (PPGs) together with spectral and prosody features to form tandem feature in the phonetic dictionary. The tandem feature allow us to estimate an activation matrix that is less dependent on source speakers, thus providing a better voice conversion quality. Second, we study the use of WaveNet vocoder that can be trained on general speech corpus from multiple speakers and adapted on target speaker data to improve the vocoding quality. We benefit from the large general speech databases that are used to train the PPG generator, and the WaveNet vocoder. The experiments show that the proposed conversion framework outperforms the traditional spectrum and prosody conversion techniques in both objective and subjective evaluations.
Ballooning Graphics Memory Space in Full GPU Virtualization Environments

Advances in virtualization technology have enabled multiple virtual machines (VMs) to share resources in a physical machine (PM). With the widespread use of graphics-intensive applications, such as two-dimensional (2D) or 3D rendering, many graphics processing unit (GPU) virtualization solutions have been proposed to provide high-performance GPU services in a virtualized environment. Although elasticity is one of the major benefits in this environment, the allocation of GPU memory is still static in the sense that after the GPU memory is allocated to a VM, it is not possible to change the memory size at runtime. This causes underutilization of GPU memory or performance degradation of a GPU application due to the lack of GPU memory when an application requires a large amount of GPU memory. In this paper, we propose a GPU memory ballooning solution called gBalloon that dynamically adjusts the GPU memory size at runtime according to the GPU memory requirement of each VM and the GPU memory sharing overhead. The gBalloon extends the GPU memory size of a VM by detecting performance degradation due to the lack of GPU memory. The gBalloon also reduces the GPU memory size when the overcommitted or underutilized GPU memory of a VM creates additional overhead for the GPU context switch or the CPU load due to GPU memory sharing among the VMs. We implemented the gBalloon by modifying the gVirt, a full GPU virtualization solution for Intel’s integrated GPUs. Benchmarking results show that the gBalloon dynamically adjusts the GPU memory size at runtime, which improves the performance by up to 8% against the gVirt with 384 MB of high global graphics memory and 32% against the gVirt with 1024 MB of high global graphics memory.
Usage Analysis : New Science Indicators and Co-usage

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Web Usage Analysis: New Science Indicators and Co-usage Xavier Polanco, Ivana Roche, Dominique Besagni
N ov 2 01 7 1 A Language for Probabilistically Oblivious Computation

An oblivious computation is one that is free of direct and indirect information leaks, e.g., due to observable differences in timing and memory access patterns. This paper presents Lobliv, a core language whose type system enforces obliviousness. Prior work on type-enforced oblivious computation has focused on deterministic programs. Lobliv is new in its consideration of programs that implement probabilistic algorithms, such as those involved in cryptography. Lobliv employs a substructural type system and a novel notion of probability region to ensure that information is not leaked via the distribution of visible events. The use of regions was motivated by a source of unsoundness that we discovered in the type system of ObliVM, a language for implementing state of the art oblivious algorithms and data structures. We prove that Lobliv’s type system enforces obliviousness and show that it is nevertheless powerful enough to check state-of-the-art, efficient oblivious data structures, such as stacks and queues, and even tree-based oblivious RAMs.
Towards Energy Efficient non-von Neumann Architectures for Deep Learning

Deep learning algorithms have taken learning-based applications by storm because of their algorithmic accuracy in modeling complex patterns, classification tasks and prediction problems across a wide range of applications like image processing, autonomous driving, pattern recognition, etc. This has come about due to several factors - improved computation capabilities with GPUs, improved memory technologies (near-data processing, High Bandwidth Memory, Hybrid Memory Cube and non-volatile memory) that have enabled us to break the memory wall), dedicated accelerators, FPGA implementations and so on. Since these workloads primarily constitute shuttling large datasets between the main memory and the processing station(s), several non-von Neumann approaches have also been implemented through dataflow computing, neuromorphic chips, Spiking neural network-based machines, and other brain-inspired computing technologies. While these technological advances have improved the accuracy of deep learning algorithms and made neuromorphic and non-von Neumann implementations possible, substantial challenges remain ahead. From a systems perspective, it is not clear how overall system level energy efficiency can be modeled, estimated and coordinated across such diverse computing architectures. Furthermore, a large class of deep learning algorithms that benefit from these advances run predominantly on “edge” devices such as in autonomous cars, and hence, overall power consumption and energy efficiency are of the highest importance. This paper makes the following contributions - we present an architectural analysis of deep learning and how they have benefited from modeling its algorithms as dataflows and representing the dataflows as “neurons”, and when modeled as non-von Neumann architectures, have drastically improved the performance and accuracy of these workloads. We then present a landscape of different CNN implementations across von Neumann and non-von Neumann models. Subsequently, we look at system level energy efficiency considerations (energy models, abstractions, estimation methodologies, standards and benchmarks) for deep learning systems built with such diverse architectures and technologies and discuss some open problems, challenges and opportunities for both industry and research communities.
Which representation is best for communicating dynamic information?

ABSTRACT The aim of this study was to investigate how expert and novice learners integrate dynamic information from two types of representations, namely auditory description and arrow symbols. Participants were required to perform both recognition and recall tests and rate their invested mental effort after studying two representations in four different orders of presentation (audio → audio, arrow → arrow, arrow → audio, or audio → arrow). When the same representation is presented twice, the results demonstrated the superiority of the spatial representation over the verbal representation for novice learners. However, when comparing the representations, it was found that novice learners achieved higher levels of performance when they had received audio first and then arrows, while expert learners showed the same level of performance regardless of the sequence of representations. Implications for improving instructional design techniques aimed at the acquisition of temporal information are discussed.
Wearable Cardiorespiratory Monitoring Device for Heart Attack Prediction

The paper aims at developing a wearable cardio respiratory monitoring device that could monitor and display 4 parameters in real time simultaneously on a phone screen or on a computer screen. A prototype system that can evaluate heart rate, respiration rate, peripheral capillary oxygen saturation (SpO2) and temperature is designed. The hardware will be cost effective and of miniaturised in size that can be worn on a daily basis. Individuals who have a history of heart attack is shown to have a constant variation in parameters like heart rate, respiration rate and peripheral capillary oxygen saturation over a time period of 1 week or even 2 weeks. Thus the data taken from individuals are stored and analysed using a mobile application. Using this information the chances of heart attack can be predicted and individuals can be warned beforehand.
Demographic inference using a particle filter for continuous Markov Jump processes

Demographic events shape a population’s genetic diversity, a process described by the coalescent-with-recombination (CwR) model that relates demography and genetics by an unobserved sequence of genealogies. The space of genealogies over genomes is large and complex, making inference under this model challenging. We approximate the CwR with a continuous-time and -space Markov jump process. We develop a particle filter for such processes, using waypoints to reduce the problem to the discrete-time case, and generalising the Auxiliary Particle Filter for discrete-time models. We use Variational Bayes for parameter inference to model the uncertainty in parameter estimates for rare events, avoiding biases seen with Expectation Maximization. Using real and simulated genomes, we show that past population sizes can be accurately inferred over a larger range of epochs than was previously possible, opening the possibility of jointly analyzing multiple genomes under complex demographic models. Code is available at https://github.com/luntergroup/smcsmc MSC 2010 subject classifications: Primary 60G55, 62M05, 62M20, 62F15; secondary 92D25.
A Stable Power Reserve Control Method in Photovoltaic Systems Using I-V Curve Characteristics

Photovoltaic power plants usually work in maximum power point tracking (MPPT) mode to maximize the output power that deliver to the grid. By increasing the number of installed PV plants, new challenges have emerged in the system such as overloading, overvoltages, and operation during voltage disturbances. So the need for increased ancillary services imposes power reserves in the photovoltaic systems. In this paper, a stable algorithm is used to operate in constant power generation (CPG) mode at the right side of the MPP without any instability problems by using I-V characteristic of the PV panel. A 100 KW PV system has been simulated in MATLAB/Simulink and the operation under a fast change in irradiance has been considered.
Controller Design of Paralleled Quasi-Z-Source Inverters with Battery for Application in Grid-Connected Photovoltaic Power Generation System

In this paper, a new control method for the paralleled quasi-Z-source inverters (qZSI) with a battery for application in grid-connected photovoltaic (PV) power generation systems is proposed. The use of paralleled inverters in distributed generation (DG) applications has several advantages, such as high degree of redundancy, ease of maintenance and high reliability. By introducing the shoot-through states in the permissible switching states of the inverter bridge, the qZSI can boost/buck the output voltage of PV panel in a wide range. Also, the energy storage system, which is assisted with quasi Z-source network, provides the full use possibility of PV power. The proposed control method can manage the battery power, extract the maximum power from the PV panel, control the inverters output power with desired power factor and maintain the balance of power injected by the paralleled inverters into the grid, simultaneously. Also, the controller system is able to suppress both types of circulating-current, including cross and zero-sequence circulating-currents through a modification of the conventional control schemes. The ability of the proposed PV system is investigated by simulation results carried out using MATLAB/Simulink.
Improved Haze Removal Method Using Proportionate Fusion of Color Attenuation Prior and Edge Preserving

Nowadays, fog and haze are becoming a global challenge. Images captured under the hazy condition have poor contrast and corrupted colour. Such images limit the visibility and thus hinder the way for the computer vision purposes like video observation, entity recognition. Such hazy images affect the usual working in the transportation sector e.g. trains, ships etc. One of the prominent factors affecting the outdoor vision applications is poor vision and thus, it may result in an intangible loss like safety. The image captured shows this behaviour because of the air light. This paper analyses the widely used techniques for haze removal from images alias color attenuation prior based haze removal and haze removal with edge preserving. Also, the paper proposes the better haze removing techniques using fusion based approach which gives better quality haze removal compared to the bench mark haze removal techniques. Experimental results tested with NIQE have proved the worth of proposed methods.
Synthesizing 3D Acoustic-Articulatory Mapping Trajectories: Predicting Articulatory Movements by Long-Term Recurrent Convolutional Neural Network

Robust and accurate predicting of articulatory movements has various important applications, such as 3D articulatory animations and visual communication. Various approaches have been proposed to solve the acoustic-articulatory mapping problem. However, their precision is not high enough. Recently, deep neural network (DNN), especially convolutional neural network (CNN) and recurrent neural network (RNN), has brought tremendous success in speech recognition and synthesis. To increase the accuracy, we propose a new network architecture for acoustic-articulatory mapping, called long-term recurrent convolutional neural network (LTRCNN). The network consists of CNN, RNN and a skip connection. CNN can model the spectral correlation among acoustic features efficiently. RNN, like long short-term memory (LSTM), can learn the temporal context information from sequential data powerfully. Besides, skip connections can increase the input representation from different levels to preserve the feature information. Experiments show that LTRCNN achieves the state-of-the-art root-mean-squared error (RMSE) with 0.690 mm and the correlation coefficient with 0.949 in this prediction task.
Source Camera Identification of Image for Forensic Analysis Using Sensor Fingerprints

The forensic analysis in mobile device becomes more important in today's world due to the quick expansion and frequent use in society. Each pictures shared through mobile phones or posted in social medias has produced a large amount of data and are used as legal framework as a source for investigating crimes. The information are stored as EXIF. In this paper we are describing about the image feature extraction by using some wavelet based features which utilizes a sensor pattern noise(SPN) and the channel seperation is done using some color model such as RGB and HDV models. Finally they are classified using SVM. The experimentation in this paper is done for images using different camera models.
Projection-Based Regularized Dual Averaging for Stochastic Optimization

We propose a novel stochastic-optimization framework based on the regularized dual averaging (RDA) method. The proposed approach differs from the previous studies of RDA in three major aspects. First, the squared-distance loss function to a “random” closed convex set is employed for stability. Second, a sparsity-promoting metric (used implicitly by a certain proportionate-type adaptive filtering algorithm) and a quadratically-weighted $\ell _1$ regularizer are used simultaneously. Third, the step size and regularization parameters are both constant due to the smoothness of the loss function. These three differences yield an excellent sparsity-seeking property, high estimation accuracy, and insensitivity to the choice of the regularization parameter. Numerical examples show the remarkable advantages of the proposed method over the existing methods (including AdaGrad and the adaptive proximal forward–backward splitting method) in applications to regression and classification with real/synthetic data.
RF-IDraw: virtual touch screen in the air using RF signals

In this extended abstract, we discuss high-level design principles and future applications that can be enabled by RF-IDraw, an RF-based trajectory tracing system originally proposed in [16]. RF-IDraw achieves trajectory tracing accuracy of a few centimeters and hence, enables novel applications in gaming, gesture based interfaces, indoor navigation and the like, using RF-signals which could not be possible with existing RF positioning systems. The accurate trajectory tracing is the result of an interesting antenna array design principle, wherein we exploit the tradeoff between ambiguity and resolution in the antenna array formulation to improve the resolution of the RF-positioning systems. The current implementation of RF-IDraw runs using commercial, off-the-shelf RFID readers and enables a realtime virtual touch screen which can be used for applications like in-the-air gesture recognition and word recognition. This application can be used in variety of ways to interact with any computing device, including, but not limited to, sensors, smartphones, laptops and projector-screens.
On-demand routing in multi-hop wireless mobile ad hoc networks

This thesis introduces a deep reactive-ion-etch (DRIE) CMOS-MEMS process which incorporates bulk Si into microstructures using backside etch. The resultant microstructures are flat and have no release holes. Electrically isolated silicon is obtained using a silicon undercut. This process is suitable for applications that require large mass and flat surfaces such as inertial sensors and micromirrors. A technique for vertical-axis sensing and actuation using comb-finger sidewall capacitance is developed. For vertical-axis sensing, this technique has small parasitic capacitance compared to the counterparts that have an electrode on the substrate. For vertical actuation, this technique has a very large gap to substrate set by the process, so that the actuation range is not limited. A unique curled comb drive design demonstrates a maximum 62 μm out-of-plane displacement for a micro-mirror scanning. Several thin-film and DRIE devices are fabricated and characterized. A 0.5 mm by 0.5 mm microstage moves 0.3 μm vertically at 14 V d.c. Thin-film and DRIE z-axis accelerometers (both are about 0.5 mm by 0.6 mm) have noise floors of 6 mg/Hz1/2 and 0.5 mg/Hz1/2, respectively. The noise floor of a lateral-axis gyroscope is improved from 0.8°/s/Hz 1/2 for the thin-film CMOS-MEMS process to 0.02°/s/Hz 1/2 for the DRIE CMOS-MEMS process. Solutions for improving the gyroscopes' performance and yield are proposed, including a new process flow for independently controlling silicon undercut and a vertical-axis electrostatic force cancellation technique for compensating the off-axis motion. The electrostatic micromirror rotates 5° at 18 V d.c. The thermally actuated micromirror rotates 17° at 12 mA current and has been installed into an endoscopic optical coherence tomography imaging system for in vivo imaging of biological tissue. Transverse and axial resolutions of roughly 20 μm and 10 μm, respectively, are achieved. Cross-sectional images of 500 x 1000 pixels covering an area of 2.9 x 2.8 mm2 are acquired at 5 frames/s.