Miracle-dz/UniFAR-FTUs huggingface dataset:
An overview of Machine Learning Technologies and their use in E-learning

Thanks to new technologies, internet, connected objects we produce a phenomenal amount of data. Putting these data in context, organizing them to be able to perceive, understand and reflect them is very important. Traditionally, human have analyzed data. However, as the volume of data surpasses, human increasingly turn to automated systems that can imitate him. Those systems able to learn from both data and changes in data in order to solve problems are called machine learning. Artificial intelligence has a major impact on e-learning research and the machine learning based methods can be implemented to improve Technology Enhanced Learning Environments (TELE). This paper is an overview of the recent findings in this research field. At first, we introduce the key concepts related to machine learning. Then, we present some recent works using machine learning in e-learning context.
Neurodata Lab’s approach to the Challenge on Computer Vision for Physiological Measurement

This paper introduces the Neurodata Lab’s approach presented at the 1st Challenge on Remote Physiological Signal Sensing (RePSS) organized within CVPR2020. The RePSS challenge was focused on measuring the average heart rate from color facial videos, which is one of the most fundamental problems in the field of computer vision.Our deep learning-based approach includes 3D spatiotemporal attention convolutional neural network for photoplethysmogram extraction and 1D convolutional neural network pre-trained on synthetic data for time series analysis. It provides state-of-the-art results outperforming those of other participants on a mixture of VIPL and OBF databases: MAE=6.94 (12.3% improvement compared to the top-2 result), RMSE=10.68 (24.6% improvement), Pearson R = 0.755 (28.2% improvement).
Robust Loop Closure Method for Multi-Robot Map Fusion by Integration of Consistency and Data Similarity

For an efficient collaboration of multi-robot system during missions, it is essential for the system to create a global map and localize the robots in it. However, the relative poses among robots may be unknown, preventing the system from generating the reference map. In such cases, the necessary information must be inferred through inter-robot loop closures, which are mainly perception-derived measurements obtained when robots observe the same place. However, as perception-derived measurements rely on the similarity of sensor data, different places could be wrongly identified as the same location if they exhibit similar appearances. This phenomenon, called perceptual aliasing, produces inaccurate loop closures that can severely distort the global map. This study presents a robust inter-robot loop closure selection for map fusion that utilizes the degrees of both consistency and data similarity of the loop closures for accurate measurement determination. We define the coalition of these information as the measurement pair score and employ it as weights in the objective function of the combinatorial optimization problem that can be solved as maximum edge weight clique from graph theory. The algorithm is tested on an experimental dataset for performance evaluation and the result is discussed in comparison to a state-of-the-art method.
Safeguarding Backscatter RFID Communication against Proactive Eavesdropping

Passive radio frequency identification (RFID) systems raise new transmission secrecy protection challenges against the special proactive eavesdropper, since it is able to both enhance the information wiretap and interfere with the information detection at the RFID reader simultaneously by broadcasting its own continuous wave (CW) signal. To defend against proactive eavesdropping attacks, we propose an artificial noise (AN) aided secure transmission scheme for the RFID reader, which superimposes an AN signal on the CW signal to confuse the proactive eavesdropper. The power allocation between the AN signal and the CW signal are optimized to maximize the secrecy rate. Furthermore, we model the attack and defense process between the proactive eavesdropper and the RFID reader as a hierarchical security game, and prove it can achieve the equilibrium. Simulation results show the superiority of our proposed scheme in terms of the secrecy rate and the interactions between the RFID reader and the proactive eavesdropper.
Identifying Optimization Opportunities Using Memory Access Tracing in OpenSHMEM Runtimes with the TAU Performance System

Exascale systems are expected to have fewer bytes of memory per core available than present petascale systems. Previous analysis of the Open MPI OpenSHMEM runtime has shown that it allocates some object types which use memory proportional to the square of the number of PEs. In this paper, we describe a technique for tracking memory accesses by OpenSHMEM runtimes. By tracking reads and writes, we can determine which object types are constant after initialization; those objects could be shared across PEs which are co-located on the same physical node. This tracking is implemented in the TAU Performance System by using user-level page fault handling in Linux.
Scene Description from Depth Images for Visually Positioning

Visually positioning of impaired and blind people is still a challenge for computer vision community. The difficulty still remains in the retrieval process which try to find the best place associated to a query image. We propose in this paper a scene descriptor suitable as a filter in the matching process between query and model frames. From a video sequence of depth images, our method based geometry, represents the scene using 2D map. First, we segment the scene from the depth image into planar regions. Using these regions, the application of registration algorithm allows obtaining 3D model of the scene. Next, we determine the ground plan independently from RGB-D camera’s pose and we select walls as the lateral and vertical plans to ground. Their projection on the ground gives the boundaries of the scene which are described geometrically. Finally, the descriptor of the obtained 2D map provides a determining selector of place classes. We applied our method on indoor scenes, the obtained results are presented and discussed.
Attention-Based Convolutional LSTM for Describing Video

Video description technique has been widely used in the computer community for many applications. The typical approaches are mainly based on the encode-decode framework: the fixed-length video representation vectors are extracted by the encoder using the upper layer output of pre-trained convolutional neural networks (CNNs); The decoder uses the recurrent neural networks to generate a textual sentence. However, the upper layers of convolutional neural networks contain low-resolution, semantically strong, while the lower layers contain high-resolution, semantically weak features. In the existing method, the multi-scale information of CNNs is hardly considered to be used in the video description. Ignoring this information will lead to the problem that the video description is not detailed and comprehensive. This paper applies the hierarchical convolutional long short-term memory (ConvLSTM) in the encode-decode framework to conduct feature extraction of the upper and lower layers in CNNs. Moreover, multiple network structures are designed to explore the Spatio-temporal feature extraction performance of ConvLSTM, which can approach the optimal accuracy in the three-layer ConvLSTM. In order to efficiently improve the language quality of video description, the attention mechanism focuses on visual feature output by ConvLSTM. The extensive experimental results demonstrate that the proposed method outperforms the existing approaches.
Simultaneous Allocation and Control of Distributed Energy Resources via Kullback-Leibler-Quadratic Optimal Control

There is enormous flexibility potential in the power consumption of the majority of electric loads. This flexibility can be harnessed to obtain services for managing the grid: with carefully designed decision rules in place, power consumption for the population of loads can be ramped up and down, just like charging and discharging a battery, without any significant impact to consumers' needs. The concept is called Demand Dispatch, and the grid resource obtained from this design virtual energy storage (VES). In order to deploy VES, a balancing authority is faced with two challenges: 1. how to design local decision rules for each load given the target aggregate power consumption (distributed control problem), and 2. how to coordinate a portfolio of resources to maintain grid balance, given a forecast of net-load (resource allocation problem). Rather than separating resource allocation and distributed control, in this paper the two problems are solved simultaneously using a single convex program. The joint optimization model is cast as a finite-horizon optimal control problem in a mean-field setting, based on the new KLQ optimal control approach proposed recently by the authors.The simplicity of the proposed control architecture is remarkable: With a large portfolio of heterogeneous flexible resources, including loads such as residential water heaters, commercial water heaters, irrigation, and utility-scale batteries, the control architecture leads to a single scalar control signal broadcast to every resource in the domain of the balancing authority. Keywords: Smart grids, demand dispatch, distributed control, controlled Markov chains.
Beam Tracking Based on unscented Kalman Filter Theory in Millimeter Wave Communication Systems for IoT

The beam coverage directivity of mmWave communication brings great challenges to the application research of high-speed Internet of things (IoT) terminal devices, such as unmanned vehicles and unmanned aerial vehicles (UAV). Most of the existing millimeter wave beam tracking algorithms aim at AOA / AOD (Angles of Arrival / Angles of Departure) for continuous tracking estimation. However, the estimation error will increase rapidly with the increase of AOA / AOD change speed. Inspired by Auxiliary Beam Pair (ABP) algorithm, a robust two-stage beam tracking algorithm is proposed in this paper. First, AOA/AOD is estimated by the Unscented Kalman filter (UKF) algorithm, and then the AOA/AOD is modified by the improved ABP algorithm. The simulation results show that when AOA / AOD changes at a speed greater than 0.25 degrees per time slot, the proposed two-step beam tracking algorithm significantly reduces the estimation error and effectively increases the robustness of the same type of algorithm.
Security Enhanced Rhythm Authentication using Relative Finger-Tip Positions

The rhythm authentication algorithm uses the concept of a traditional keystroke authentication and a multitouch technology based on a touchable device. Three measurements are used in the rhythm authentication method which consists of holding times, latency times, and the number of fingers per beat. These measurements are compared with the user templates. The user is authenticated if a percentage of error is in a predetermined range. However, using only three measurements is not enough. If the attacker is familiar with the victim, the rhythm authentication can be attacked by shoulder surfing or eavesdropping. Additionally, only a percentage of similarity between the user template in database and user’s input for classifying is not a reliable algorithm. In this research, we propose a security-enhanced rhythm authentication using relative finger-tip positions and the KNN algorithm for classification to prevent shoulder-surfing attacks.
Recommender systems based on detection community in academic social network

The speed with which new scientific articles are published and shared on academic social networks generated a situation of cognitive overload and the targeted access to the relevant information represents a major challenge for researchers. In this context, we propose a scientific article recommendation approach based on the discovery of thematic community structures, it focuses on the topological structure of the network combined with the analysis of the content of the social object (scientific article), a strategy that aims to mitigate the cold start problems and sparcity data in scoring matrix. A key element of our approach is the modeling of the researcher's thematic centers of interest derived from his corpus (a set of articles that interested him). In this perspective we use the technique of semantic exploration and extraction of latent topics in document corpora, LDA(Latent DirichletAllocation), an unsupervised learning method which offers the best solution of scalability problemcompared to other techniques of topic modeling. this technique allows us to build a profile model in the form of vectors in which the components are the probabilistic distributions on topics that reflect the interests of the researcher. The profile models thus constructed will be grouped into thematic clusters based on dominant topics using the fuzzy clustering algorithm, since the same topic can be treated in different scientific fields. Will follow a step of detection of community structures in thematic clusters to identify significant communities, the aim of this step is to project the recommendation process in a small space allowing better performance by reducing the computation time and the storage space for researcher/article data. The preliminary results of the experience of our approach on a population of 13 researchers and 60 articles shows that the articles generated by the recommendation process are very relevant to the target researcher or his community.
Mimosa: Protecting Private Keys Against Memory Disclosure Attacks Using Hardware Transactional Memory

Cryptography is essential for computer and network security. When cryptosystems are deployed in computing or communication systems, it is extremely critical to protect the cryptographic keys. In practice, keys are loaded into the memory as plaintext during cryptographic computations. Therefore, the keys are subject to memory disclosure attacks that read unauthorized data from RAM. Such attacks could be performed through software exploitations, such as OpenSSL Heartbleed, even when the integrity of the victim system’s binaries is maintained. They could also be done through physical methods, such as cold-boot attacks, even if the system is free of software vulnerabilities. This paper presents Mimosa, to protect RSA private keys against both software-based and physical memory disclosure attacks. Mimosa uses hardware transactional memory (HTM) to ensure that (a) whenever a malicious thread other than Mimosa attempts to read the plaintext private key, the transaction aborts and all sensitive data are automatically cleared with hardware, due to the strong atomicity guarantee of HTM; and (b) all sensitive data, including private keys and intermediate states, appear as plaintext only within CPU-bound caches, and are never loaded to RAM chips. To the best of our knowledge, Mimosa is the first solution to use transactional memory to protect sensitive data against memory attacks. However, the fragility of TSX transactions introduces extra cache-clogging denial-of-service (DoS) threats, and attackers could sharply degrade the performance by concurrent memory-intensive tasks. To mitigate the DoS threats, we further partition an RSA private-key computation into multiple transactional parts by analyzing the distribution of aborts, while (sensitive) intermediate results are still protected across transactional parts. Through extensive experiments, we show that Mimosa effectively protects cryptographic keys against attacks that attempt to read sensitive data in memory, and introduces only a small performance overhead, even with concurrent cache-clogging workloads.
Selective Tracking Using Linear Trackability Analysis and Inversion-based Tracking Control

Trackability is the ability of a system to follow arbitrary reference commands and is equivalent to a system being right invertible. For systems that are trackable, feedforward inversion-based control (along with an additional feedback loop) is a common method to achieve tracking. In this paper, we focus on trackability for linear discrete-time MIMO systems and examine the idea of selective tracking that is particularly useful when a system is not trackable but it is possible to track either certain subsets of outputs or certain combinations of them. We show that tracking each component of the output vector can be assigned a priority within the context of an inversion-based controller to provide significant flexibility in selectively tracking outputs of interest even when the system as a whole is untrackable. We further demonstrate various aspects of trackability theory and selective tracking through a few simulation examples including a quadrotor example.
Late laying hens deposit dietary antioxidants preferentially in the egg and not in the body

Abstract The allocation of nutrients in the metabolism of laying hens favors the egg over the body. It is unclear whether this programming also includes micronutrients and antioxidants. This was tested with five by seven 76-wk-old Heisdorf & Nelson Brown Nick layers. They were fed a basal diet low in antioxidants either unchanged (control) or supplemented with 40 IU of α-tocopherylacetate/kg of diet (vitamin E) or 2.5% chokeberry pomace, rose hip, or sage in the last 4 wk before slaughter. The traits measured were subjected to ANOVA. The additives did not affect ADFI, performance, egg quality, carcass, or meat quality. Yolk tocopherol contents were higher with vitamin E and sage compared with control and rose hip treatments. Oxidative stability was more affected in egg yolk powder than in the meat. After 12 wk of storage, yolks from vitamin E-treated hens were lowest in TBA levels (5.0 mg of malondialdehyde/kg), followed by chokeberry (8.7) and sage (8.8). Rose hip (18.1) and control (18.9) treatments were similar. In meat, TBA was slightly decreased after 9 d of storage in meat from chokeberry- and sage-supplemented hens (contrast analysis). In conclusion, antioxidant deposition to the egg seems to be part of the genetic determination of the hen. Adding antioxidants is therefore interesting for layer nutrition in general, and herbal additives allow performing this in a natural way. The programming for partitioning, however, renders the strategic feeding of antioxidants before slaughter, with the goal to increase oxidative stability of spent hen meat rather inefficient.
Unlicensed Spectrum-Sharing Mechanism Based on Wi-Fi Security Requirements Implemented Using Device to Device Communication Technology

Rational use of unlicensed spectrum resources and the use of device-to-device (D2D) communication technologies are two essential technologies for improving the user experience and spectrum efficiency of future communication systems. In this paper, we propose an unlicensed spectrum-sharing scheme that targets the security needs of Wi-Fi users. Unlike the current unlicensed spectrum coexistence mechanism, we change “coexistence by competition” to “coexistence by sharing” and use D2D communication technology to share unlicensed spectrum resources with Wi-Fi users. This coexistence scheme allows the Wi-Fi system to surrender a portion of its spectrum resources in a way that allows cochannel interference when there is a security requirement by presenting the expected security requirements. Our analytical model and simulation experiments show that this method takes advantage of D2D proximity communication, provides more unlicensed spectrum resources to cellular users with less performance degradation for Wi-Fi users, and improves the total spectrum efficiency of the system.
A Dynamic Quasi-Taylor Approach for Distributed Consensus Problems with Packet Loss

This paper presents a novel approach for handling packet loss in first-order consensus protocols based on a Taylor series expansion. We propose a dynamic memory approach which depends on a locally measured loss rate at each agent. The quasi-Taylor method assumes that each agent is storing not only the past received value of its neighbours in a memory, but the last ν received states of all neighbours in order to predict the future trajectory with the quasi-Taylor estimation. In addition, we use the so-called importance measure to label the most important information received at each time step. Then, depending on the measured loss rate the past data points of a neighbour are used to predict the future trajectory. Hereby, the trajectory is determined as a convex combination of different orders of the quasi-Taylor estimation. In order to minimise the distance of the consensus value to the actual average, we propose to use an adaptive step size for predicting the future trajectory of the neighbours. We give an upper bound on the convergence rate when uniform packet loss is assumed and show that the proposed approach outperforms existing methods from the literature with the help of simulation studies.
Analysis of Online Parameter Estimation for Electrochemical Li-ion Battery Models via Reduced Sensitivity Equations

This paper focuses on the problem of online parameter estimation in an electrochemical Li-ion battery model. Online parameter estimation is necessary to account for model mismatch, environmental disturbances, and cycle-induced aging in Li-ion battery models. Sensitivity analysis can improve parameter estimation by identifying which data the parameters are most sensitive to. However, computing parameter sensitivity in full-order electrochemical models is typically intractable for online applications. Using a reduced-order model can lower the computational burden and, as we demonstrate, approximates well the sensitivity of the higher-order model. To provide further insight into the parameter estimation challenge, we analyze the effect that identifying parameters according to voltage RMSE data has on internal state errors. We perform a simulation study which demonstrates that parameter estimation approaches based on this paradigm are not sufficient for safe battery operation or other control objectives that require accurate estimates of these states.
UAV for Wireless Power Transfer in IoT Networks: A GMDP approach

Unmanned aerial vehicles (UAVs) are a promising technology employed as moving aggregators and wireless power transmitters for IoT networks. In this paper, we consider an UAV-IoT wireless energy and data transmission system and the decision-making problem is investigated. We aim at optimizing the nodes’ utilities by defining a good packet delivery and energy transfer policy according to the system state. We formulate the problem as a Markov Decision Process (MDP) to tackle the successive decision issues. As the MDP formalism achieves its limits when the neighbors’ interactions are considered, we formulate the problem as a Graph-based MDP (GMDP). We then propose a Mean-Field Approximation (MFA) algorithm to find a solution. The simulation results show that our framework achieves a good analysis of the system behavior.