Miracle-dz/UniFAR-FTUs huggingface dataset:
Keeping the Smart Home Private with Smart(er) IoT Traffic Shaping

Abstract The proliferation of smart home Internet of things (IoT) devices presents unprecedented challenges for preserving privacy within the home. In this paper, we demonstrate that a passive network observer (e.g., an Internet service provider) can infer private in-home activities by analyzing Internet traffic from commercially available smart home devices even when the devices use end-to-end transport-layer encryption. We evaluate common approaches for defending against these types of traffic analysis attacks, including firewalls, virtual private networks, and independent link padding, and find that none sufficiently conceal user activities with reasonable data overhead. We develop a new defense, “stochastic traffic padding” (STP), that makes it difficult for a passive network adversary to reliably distinguish genuine user activities from generated traffic patterns designed to look like user interactions. Our analysis provides a theoretical bound on an adversary’s ability to accurately detect genuine user activities as a function of the amount of additional cover traffic generated by the defense technique.
A Survey on Various Median Filtering Techniques For Removal of Impulse Noise From Digital Image

The elimination of noise from images becomes a trending field in image processing. Imagesmay got corrupted by random change in pixel intensity, illumination, or due to poor contrast and can't be used directly. Therefore, it is necessary to get rid of impulse noise presented inan image. In order to remove such impulse noise, Median based filters are commonly used. However, we use various types of median filters such as recursive median filter, iterative median filter, directional median filter, weighted median filter, adaptive median filter progressive switching median filter and threshold median filter. This paper will survey various median filtering techniques for excluding noisy pixel from a digital image.
Joint Semi-supervised Learning and Re-ranking for Vehicle Re-identification

Vehicle re-identification (re-ID) remains an unproblematic problem due to the complicated variations in vehicle appearances from multiple camera views. Most existing algorithms for solving this problem are developed in the fully-supervised setting, requiring access to a large number of labeled training data. However, it is impractical to expect large quantities of labeled data because the high cost of data annotation. Besides, re-ranking is a significant way to improve its performance when considering vehicle re-ID as a retrieval process. Yet limited effort has been devoted to the research of re-ranking in the vehicle re-ID. To address these problems, in this paper, we propose a semi-supervised learning system based on the Convolutional Neural Network (CNN) and re-ranking strategy for Vehicle re-ID. Specifically, we adopt the structure of Generative Adversarial Network (GAN) to obtain more vehicle images and enrich the training set, then a uniform label distribution will be assigned to the unlabeled samples according to the Label Smoothing Regularization for Outliers (LSRO), which regularizes the supervised learning model and improves the performance of re-ID. To optimize the re-ID results, an improved re-ranking method is exploited to optimize the initial rank list. Experimental results on publically available datasets, VeRi-776 and VehicleID, demonstrate that the method significantly outperforms the state-of-the-art.
An Improved Random Path Length Algorithm for p-i-n and Staircase Avalanche Photodiodes

We present an improved Random Path Length algorithm to accurately and efficiently estimate the design space of heterostructure avalanche photodiodes (APDs) in terms of gain, noise and bandwidth without any need of full Monte Carlo transport simulations. The underlying nonlocal model for impact ionization goes beyond the Dead Space concept and it is suited to handle staircase structures composed by a superlattice of III-V compounds as well as thick and thin p-i-n APDs. The model parameters have been calibrated on GaAs and $Al_{x}Ga_{1-x}As$ p-i-n APDs in a previous work. In this work GaAs p-i-n APDs are compared to staircase structures in terms of noise and bandwidth.
Role of the Carotid Bodies in the Hypertensive and Natriuretic Responses to NaCl Load in Conscious Rats

Hyperosmotic challenges trigger a hypertensive response and natriuresis mediated by central and peripheral sensors. Here, we evaluated the importance of the carotid bodies for the hypertensive and natriuretic responses to acute and sub-chronic NaCl load in conscious rats. Male Wistar rats (250–330 g) submitted to bilateral carotid body removal (CBX) or sham surgery were used. One day after the surgery, the changes in arterial blood pressure (n = 6–7/group) and renal sodium excretion (n = 10/group) to intravenous infusion of 3 M NaCl (1.8 mL/kg b.w. during 1 min) were evaluated in non-anesthetized rats. Another cohort of sham (n = 8) and CBX rats (n = 6) had access to 0.3 M NaCl as the only source of fluid to drink for 7 days while ingestion and renal excretion were monitored daily. The sodium balance was calculated as the difference between sodium infused/ingested and excreted. CBX reduced the hypertensive (8 ± 2 mmHg, vs. sham rats: 19 ± 2 mmHg; p < 0.05) and natriuretic responses (1.33 ± 0.13 mmol/90 min, vs. sham: 1.81 ± 0.11 mmol/90 min; p < 0.05) to acute intravenous infusion of 3 M NaCl, leading to an increase of sodium balance (0.38 ± 0.11 mmol/90 min, vs. sham: -0.06 ± 0.10 mmol/90 min; p < 0.05). In CBX rats, sub-chronic NaCl load with 0.3 M NaCl to drink for 7 days increased sodium balance (18.13 ± 4.45 mmol, vs. sham: 5.58 ± 1.71 mmol; p < 0.05) and plasma sodium concentration (164 ± 5 mmol/L, vs. sham: 140 ± 7 mmol/L; p < 0.05), without changing arterial pressure (121 ± 9 mmHg, vs. sham: 116 ± 2 mmHg). These results suggest that carotid bodies are important for the maintenance of the hypertensive response to acute hypertonic challenges and for sodium excretion to both acute and chronic NaCl load.
Asynchronous and Event-Based Fusion Systems for Affect Recognition on Naturalistic Data in Comparison to Conventional Approaches

Throughout many present studies dealing with multi-modal fusion, decisions are synchronously forced for fixed time segments across all modalities. Varying success is reported, sometimes performance is worse than unimodal classification. Our goal is the synergistic exploitation of multimodality whilst implementing a real-time system for affect recognition in a naturalistic setting. Therefore we present a categorization of possible fusion strategies for affect recognition on continuous time frames of complete recording sessions and we evaluate multiple implementations from resulting categories. These involve conventional fusion strategies as well as novel approaches that incorporate the asynchronous nature of observed modalities. Some of the latter algorithms consider temporal alignments between modalities and observed frames by applying asynchronous neural networks that use memory blocks to model temporal dependencies. Others use an indirect approach that introduces events as an intermediate layer to accumulate evidence for the target class through all modalities. Recognition results gained on a naturalistic conversational corpus show a drop in recognition accuracy when moving from unimodal classification to synchronous multimodal fusion. However, with our proposed asynchronous and event-based fusion techniques we are able to raise the recognition system's accuracy by 7.83 percent compared to video analysis and 13.71 percent in comparison to common fusion strategies.
Living with Artificial Intelligence: A Paradigm Shift toward Future Network Traffic Control

Future Internet is expected to meet explosive traffic growth and extremely complex architecture, which tend to make the traditional NTC strategies inefficient and even ineffective. Inspired by the latest breakthroughs of AI and its power to address large-scale and complex difficulties, the network community has begun to consider shifting the NTC paradigm from legacy rule-based to novel AI-based. As an applied inter-discipline, design and implementation are important. Although there have been some preliminary explorations along this frontier, they are either limited by only envisioning the prospects, or too scattered to provide high-level insight into a general methodology. To this end, we start with the domain knowledge relationships of AI and NTC, summarizing a baseline workflow toward deep reinforcement learning, which will be the dominant method for the AI-NTC paradigm. On top of that, we argue that AI-NTC training and running must be carried out in online environments in closed-loop fashion for the purpose of putting ti into practice. A series of challenges and opportunities are discussed from a realistic viewpoint, and a set of new architecture and mechanism to enable the online and closed-loop AI-NTC paradigm are proposed. Hopefully, this work can help the AI community to better understand NTC and the NTC community to better live with AI.
Sports Classification in Sequential Frames Using CNN and RNN

Automatic sports classification is basic but important task for archiving digital contents in broadcasting companies as well as for general video scene understanding. In this paper, deep learning approach combining convolutional and recurrent neural networks is applied to classify five different classes of sports. Features extracted from CNN are combined temporal analysis using RNN. Multiple experiments results show that the effect of different frame sequence correctly classified test data up to 96.66%.
SynRhythm: Learning a Deep Heart Rate Estimator from General to Specific

Remote photoplethysmography (rPPG) based noncontact heart rate (HR) measurement from a face video has drawn increasing attention recently because of its potential applications in many scenarios such as training aid, health monitoring, and nursing care. Although a number of methods have been proposed, most of them are designed under certain assumptions and could fail when such assumptions do not hold. At the same time, while deep learning based methods have been reported to achieve promising results in many computer vision tasks, their use in rPPG-based heart rate estimation has been limited due to the very limited data available in public domain. To overcome this limitation and leverage the strong modeling ability of deep neural networks, in this paper, we propose a novel spatial-temporal representation for the HR signal and design a general-to-specific transfer learning strategy to train a deep heart rate estimator from a large volume of synthetic rhythm signals and a limited number of available face video data. Experiment results on the public-domain databases show the effectiveness of the proposed approach.
Nose, Eyes and Ears: Head Pose Estimation by Locating Facial Keypoints

Monocular head pose estimation requires learning a model that computes the intrinsic Euler angles for pose (yaw, pitch, roll) from an input image of human face. Annotating ground truth head pose angles for images in the wild is difficult and requires ad-hoc fitting procedures (which provides only coarse and approximate annotations). This highlights the need for approaches which can train on data captured in controlled environment and generalize on the images in the wild (with varying appearance and illumination of the face). Most present day deep learning approaches which learn a regression function directly on the input images fail to do so. To this end, we propose to use a higher level representation to regress the head pose while using deep learning architectures. More specifically, we use the uncertainty maps in the form of 2D soft localization heatmap images over five facial key-points, namely left ear, right ear, left eye, right eye and nose, and pass them through an convolutional neural network to regress the head-pose. We show head pose estimation results on two challenging benchmarks BIWI and AFLW and our approach surpasses the state of the art on both the datasets.
Low-Rank Channel and Interference Estimation in mm- Wave Massive Antenna Arrays

Millimeter wave (mm-Wave) communications are characterized by wideband channels with few directional paths, mostly in line-of-sight. Antenna arrays are mandatory to cope with severe path-loss, and the resulting channel response is sparse in the space-time (ST) domain. This paper addresses the sparsity by proposing a channel estimation method that exploits the algebraic structure of channel and interference, without requiring complex antenna-array calibration procedures. The method relies on the recognition that the ST channel is low-rank and exhibits slowly and fast-varying features (angles/delays of arrival and fading amplitudes, respectively) and, accordingly, that the interference has a slowly-varying spatial covariance with fast-varying amplitudes. The accuracy of the estimation of quasi-stationary components is increased by introducing averaging mechanisms over multiple sequences. Numerical results show that: i) rank-1 is an effective channel-interference representation in mm- Wave setting with severe interference; ii) fundamental limits (derived in closed form) prove the remarkable performance gains in terms of signal-to interference ratio; iii) circular array arrangement with directive elements is preferable compared to square or triangular configurations.
Legitimizing Digital Transformation: From System Integration to Platformization

Past studies have elucidated how incumbents struggle to adapt to their environments when experiencing technological shifts. Yet, less is known about incumbents' pro-active usage of emerging information technologies to offer discontinuous innovations themselves. Offering discontinuous innovations, which deviate from past strengths, raise questions of internal legitimacy, i.e. perceptions of appropriateness among members, especially when still profiting from the current business model. We conducted an ethnography in an IT company to explore how internal legitimacy emerges within an incumbent which embeds a new technology to shift its business model from being an individual-service to a platform-based provider. Our results suggest that organizational members only perceive the shift as legitimate, after positive cues from the external environment emerged, despite preceding managerial persuasion attempts. By depicting the emergence of a vicious and a virtuous circle in the course of members' legitimizing, we contribute to both, literatures on innovation management and platformization.
Petroleum Hydrocarbon-Degrading Bacteria for the Remediation of Oil Pollution Under Aerobic Conditions: A Perspective Analysis

With the sharp increase in population and modernization of society, environmental pollution resulting from petroleum hydrocarbons has increased, resulting in an urgent need for remediation. Petroleum hydrocarbon-degrading bacteria are ubiquitous in nature and can utilize these compounds as sources of carbon and energy. Bacteria displaying such capabilities are often exploited for the bioremediation of petroleum oil-contaminated environments. Recently, microbial remediation technology has developed rapidly and achieved major gains. However, this technology is not omnipotent. It is affected by many environmental factors that hinder its practical application, limiting the large-scale application of the technology. This paper provides an overview of the recent literature referring to the usage of bacteria as biodegraders, discusses barriers regarding the implementation of this microbial technology, and provides suggestions for further developments.
Improved Learning in Convolutional Neural Networks with Shifted Exponential Linear Units (ShELUs)

The Exponential Linear Unit (ELU) has been proven to speed up learning and improve the classification performance over activation functions such as ReLU and Leaky ReLU for convolutional neural networks. The reasons behind the improved behavior are that ELU reduces the bias shift, it saturates for large negative inputs and it is continuously differentiable. However, it remains open whether ELU has the optimal shape and we address the quest for a superior activation function. We use a new formulation to tune a piecewise linear activation function during training, to investigate the above question, and learn the shape of the locally optimal activation function. With this tuned activation function, the classification performance is improved and the resulting, learned activation function shows to be ELU-shaped irrespective if it is initialized as a RELU, LReLU or ELU. Interestingly, the learned activation function does not exactly pass through the origin indicating that a shifted ELU-shaped activation function is preferable. This observation leads us to introduce the Shifted Exponential Linear Unit (ShELU) as a new activation function. Experiments on Cifar-100 show that the classification performance is further improved when using the ShELU activation function in comparison with ELU. The improvement is achieved when learning an individual bias shift for each neuron.
An entropy-based approach for shape description

In this paper an automatic method for the selection of those Fourier descriptors which better correlate a 2D shape contour is presented. To this aim, shape description has been modeled as a non linear approximation problem and a strict relationship between transform entropy and the sorted version of the transformed analysed boundary is derived. As a result, Fourier descriptors are selected in a hierarchical way and the minimum number of coefficients able to give a nearly optimal shape boundary representation is automatically derived. The latter maximizes an entropic interpretation of a complexity-based similarity measure, i.e. the normalized information distance. Preliminary experimental results show that the proposed method is able to provide a compact and computationally effective description of shape boundary which guarantees a nearly optimal matching with the original one.
Fotodetectores de radiação infravermelha baseados em pontos quânticos de submonocamada

In this work, we investigated a new type of infrared photodetector based on InAs sub-monolayer quantum dots grown by molecular beam epitaxy (MBE). Their properties were compared with those of photodetectors containing conventional InAs quantum dots obtained by the same deposition technique, but in the Stranski-Krastanov growth mode. Dark current, noise, responsivity and absorption measurements have shown that, depending on the structure of the samples, the devices with sub-monolayer quantum dots can perform very well.
Multi-Gradient Directional Features for Gender Identification

Gender identification based on handwriting analysis has received a special attention to researchers in the field of document image analysis as it is useful for several real-time applications like forensic, population counting, etc. In this paper, we explore Multi-Gradient Directional (MGD) features, which provide direction of dominant pixels obtained by Canny edge image, and gradient direction symmetry. The proposed method further performs histogram operation for gradient angle information of dominant pixels of respective multi-gradient directional images to select angles, which contribute to the highest peak. This results in feature vectors. The process of feature vector formation continues for the segmented first, second, and third text lines in each image by male or female. Next, correlation is estimated for the vector of the first line with successive lines until converging or diverging criteria is met. If the convergence happens, a document is considered as by female, else is considered as by male. The method is tested on our own dataset, which includes images of different scripts, writers, papers, pens, and ages, and the standard database QUWI which includes Arabic and English texts, to demonstrate the efficiency of the proposed method. Comparative studies with the state of the art methods show that the proposed method is effective and useful.
FingerSlide: Investigating Passive Haptic Sliding as a Tacton Channel

The haptic sensation of sliding a surface under a probing finger can be used to convey surface information or coded data to the user. In this paper, we investigate users' ability to discern different sliding profiles based on the velocity and direction of sliding for use as haptic-tactons. We built FingerSlide, a novel haptic device which can position and control moving surfaces under a user's finger and used this to run two independent studies. The first study investigates if users can identify the direction of sliding at different velocities. The second study investigates if the users can distinguish a difference between two velocities. Our results show a faster response for higher velocities in the direction study and high error rates in identifying differences in the direction study. We discuss these results and infer design considerations for haptic devices that use the sliding effect to convey information.
Modeling Channel Length Scaling Impact on NBTI in RMG Si p-FinFETs

Negative Bias Temperature Instability (NBTI) stress and recovery time kinetics fromReplacement Metal Gate (RMG) High-K Metal Gate (HKMG) p-channel FinFETs are measured and modeled. The impact of channel length (L) scaling on shift in threshold voltage ($\mathrm{V}_{T})$,its power-law time exponent (n), Voltage Acceleration Factor (VAF) and Temperature (T) activation $( \mathrm{E}_{A})$ is analyzed. TCAD and band structure calculations are utilized to explain the L dependence of experimental data.
Markov chain Monte Carlo Methods For Lattice Gaussian Sampling: Lattice Reduction and Decoding Optimization

Sampling from the lattice Gaussian distribution has emerged as an important problem in coding, decoding and cryptography. In this paper, lattice reduction technique is adopted to Gibbs sampler for lattice Gaussian sampling. Firstly, with respect to lattice Gaussian distribution, the convergence rate of systematic scan Gibbs sampling is derived and we show it is characterized by the Hirschfeld-Gebelein-R\'enyi (HGR) maximal correlation among the multivariate of being sampled. Therefore, lattice reduction is applied to formulate an equivalent lattice Gaussian distribution but with less correlated multivariate, which leads to a better Markov mixing due to the enhanced convergence rate. Then, we extend the proposed lattice-reduction-aided Gibbs sampling to lattice decoding, where the choice of the standard deviation for the sampling is fully investigated. A customized solution that suits for each specific decoding case by Euclidean distance is given, thus resulting in a better trade-off between Markov mixing and sampler decoding. Moreover, based on it, a startup mechanism is also proposed for Gibbs sampler decoding, where decoding complexity can be reduced without performance loss. Simulation results based on large-scale MIMO detection are presented to confirm the performance gain and complexity reduction.