Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced Convolutional Neural Network for Abnormal Event Detection in Video Streams

Abnormal event detection is a complex task in computer vision. It is one among the most difficult challenges in crowded scene analysis, given the huge variety of events to analyze and the frequent occlusions that can occur. Ideally, to meet this challenge, it would be necessary to have a large number of normal and abnormal training examples. However, the high variability between events and their complexity make it almost impossible to provide enough abnormal training samples. To overcome these constraints, the solution may reside in a robust one-class learning algorithm. In this paper, we propose a novel one-class neural network technique suitable for outliers detection. This network takes advantage of the deep learning ability in image processing, while meeting the constraint of using only training samples from the target class. Our network is composed of a Convolutional Auto Encoder (CAE) with two loss functions. The first one is a standard mean squared error (MSE) and the second is a proposed compactness loss function particularly adapted for one class learning. This network allows us to not only extract robust spatiotemporal features but also ensures that all the target class representations are sufficiently compact to be effectively isolated from outliers. The proposed algorithm is evaluated on the challenging USCD Ped2 dataset for abnormal video event detection and localization. Both qualitative and quantitative experiments show competitive performances compared to the state-of-the-art methods.
Very Low Power Neural Network FPGA Accelerators for Tag-Less Remote Person Identification Using Capacitive Sensors

Human detection, identification, and monitoring are essential for many applications aiming to make smarter the indoor environments, where most people spend much of their time (like home, office, transportation, or public spaces). The capacitive sensors can meet stringent privacy, power, cost, and unobtrusiveness requirements, they do not rely on wearables or specific human interactions, but they may need significant on-board data processing to increase their performance. We comparatively analyze in terms of overall processing time and energy several data processing implementations of multilayer perceptron neural networks (NNs) on board capacitive sensors. The NN architecture, optimized using augmented experimental data, consists of six 17-bit inputs, two hidden layers with eight neurons each, and one four-bit output. For the software (SW) NNimplementation, we use two STMicroelectronics STM32 low-power ARM microcontrollers (MCUs): one MCU optimized for power and one for performance. For hardware (HW) implementations, we use four ultralow-power field-programmable gate arrays (FPGAs), with different sizes, dedicated computation blocks, and data communication interfaces (one FPGA from the Lattice iCE40 family and three FPGAs from the Microsemi IGLOO family). Our shortest SW implementation latency is 54.4 $\mu \text{s}$ and the lowest energy per inference is 990 nJ, while the shortest HW implementation latency is $1.99~\mu \text{s}$ and the lowest energy is 39 nJ (including the data transfer between MCU and FPGA). The FPGAs active power ranges between 6.24 and 34.7 mW, while their static power is between 79 and 277 $\mu \text{W}$ . They compare very favorably with the static power consumption of Xilinx and Altera low-power device families, which is around 40 mW. The experimental results show that NN inferences offloaded to external FPGAs have lower latency and energy than SW ones (even when using HW multipliers), and the FPGAs with dedicated computational blocks (multiply-accumulate) perform best.
Predicting Stock Prices using Ensemble Learning and Sentiment Analysis

The recent success of the application of Artificial Intelligence in the financial sector has resulted in more firms relying on stochastic models for predicting the behaviour of the market. Everyday, quantitative analysts strive to attain better accuracies from their machine learning models for forecasting returns from stocks. Support Vector Machine (SVM) and Random Forest based regression models are known for their effectiveness in accurately predicting closing prices. In this work, we propose a technique for analyzing and predicting stock prices of companies using the aforementioned algorithms as an ensemble. Datasets from India's National Stock Exchange (NSE) containing basic market price information are preprocessed to include well known leading technical indicators as features. Feature selection, which ranks features based on their degree of influence on the final closing price has been incorporated to reduce the size of the training dataset. Additionally, we evaluate the effectiveness of considering the public opinion of a company by employing sentiment analysis. Using a trained Word2Vec model, company specific hash-tagged posts from Twitter are classified as positive or negative. Our proposed ensemble model is then trained on a new dataset which combines the technical indicator data along with the aggregated number of positive/negative tweets of a company over time. Our experiments indicate that in some scenarios, the ensemble model performs better than the constituent models and is highly dependent of the nature and size of the training data. However, combining technical indicator data with aggregated positive/negative tweet counts has a negligible effect on the performance of the ensemble model.
A Margin Adjustable Amplifier Circuit for RRAM Read Access

In order to solve the problems of delay and crosstalk in RRAM read process, a sense amplifier (SA) circuits with adjustable margin for RRAM read access is designed. The circuit includes delay circuit, sense preamplifier circuit, and read circuit. Based on the characteristics of differential signal on bitline of RRAM during read access, a carefully controlled timing sequence is designed in the new Sense Amplifier (SA) circuit. With the help of a 4-1 delay circuit, the SA can adjust the pre-amplification of differential voltage between bitlines and improve the reliability of read access. The simulation results show that the scheme can successfully identify the high resistance state and low resistance state of the memory cell and output “0” or “1”, solving the problems of delay and crosstalk in read process.
Data and Task Offloading in Collaborative Mobile Fog-Based Networks

The rapid growth of mobile devices in recent years has led to the rapid progress of mobile computing. However, this has exposed certain limitations that have, first, been addressed by mobile cloud computing. Once the Internet-of-Things devices have started being put online, a new step in the evolution of mobile networks was taken through the addition of edge and fog computing, where small nodes at the edge of the network take up some of the load on the cloud backend. Nevertheless, even this model has shown some limitations, which is why in this paper, we address the problem of off-loading data and computations from a mobile device to the cloud to fog nodes, or to other mobile devices in the vicinity. The novelty of our proposal is the addition of a layer composed exclusively of mobile devices that collaborate in an opportunistic fashion, as a first resort when needing some computations to be off-loaded. Through a thorough analysis using the MobEmu mobile network simulator, we show that our solution is able to reduce total computation time by as much as 19%, decrease the cloud usage with up to 40%, and reduce battery consumption with more than 6%.
Authentication Technology of Mobile Internet of Things Based on the Dynamic Password

After describing the security issues that RFID authentication technology encounters at large, this paper proposes a RFID mutual authentication technology to resolve fake attacks, heavy attacks and other security issues and describes the Authentication Technology. The method refers to tags privacy level between tag and reader to achieve mutual authentication, which cannot only enhance the privacy protection of the label carrier and protect the identity privacy of the reader holder, but has effectiveness in some degree.
New Recommendation Algorithm of Valuation Filling Based on Community Filtering

For the data sparsity problem in the traditional collaborative filtering algorithm, this paper proposes a new recommendation algorithm of valuation filling based on community filtering. This algorithm confirms the "user rating scale" and "commodity popularity" in the similar user group of each kind, so as to achieve more accurate valuation calculation. And then it improves the quality of recommendation. Finally, the new algorithm's feasibility and effectiveness are verified by specific experiments, and it has better recommendation effect on sparse data sets.
Automatic Segmentation of River and Land in SAR Images: A Deep Learning Approach

The ubiquitousness of satellite imagery and powerful, computationally efficient Deep Learning frameworks have found profound use in the field of remote sensing. Augmented with easy access to abundant image data made available by different satellites such as LANDSAT and European Space Agency's Copernicus missions, deep learning has opened various avenues of research in monitoring the world's oceans, land, rivers, etc. One significant problem in this direction is the accurate identification and subsequent segmentation of surface-water in images in the microwave spectrum. Typically, standard image processing tools are used to segment the images which are time inefficient. However, in recent years, deep learning methods for semantic segmentation is the preferred choice given its high accuracy and ease of use. This paper proposes the use of deep-learning approaches such as U-Net to perform an efficient segmentation of river and land. Experimental results show that our approach achieves vastly superior performance on SAR images with pixel accuracy of 0.98 and F1 score of 0.99.
Assessment of Purposeful Movements for Post-Stroke Patients in Activites of Daily Living with Wearable Sensor Device

Hemiparesis is one of the most frequent poststroke conditions, which causes muscle weakness and/or inability to move one side of the body. Physical rehabilitation is the main treatment for hemiparesis recovery, and physiotherapists agree that using the impaired arm in the activities of daily living (ADLs)is crucial for a complete recovery. Currently, rehabilitation is assessed through diaries and self-questionnaires, which are subjective and do not tell the real condition of the patients throughout the day. Assistive devices can objectively evaluate the functional improvement of the impaired arm monitoring its activity. This work aimed to identify the purposeful arm movements during patient's ADLs. We consider arm's swing while walking as a non-purposeful movement. Firstly, the event-based approach was applied to separate movement and non-movement segments. Secondly, movement segments were used to detect the change-point (events)and their locations in time series signal. Two machine learning classifiers, Support Vector Machine (SVM)and Artificial Neural Network (ANN), were trained using 10-fold cross validation for the classification of purposeful and non-purposeful movements. Data from 10 healthy and 12 post-strokes volunteers from Institute Guttmann (Barcelona)were collected using the SensHand device. The volunteers, wearing one SensHand on each wrist, performed the following activities: resting, eating, pouring water, drinking, brushing, folding towel, grasp towel, grasp brush, grasp glass, continuous and walking. Developing a model based on the healthy subjects, the overall classification accuracy obtained from SVM classifier and ANN was 81.21 % and 97.06% respectively. Similarly, with poststroke subjects obtained accuracy with the SVM and ANN was 84.18% and 99.74% respectively. Considering the whole dataset, SVM and ANN obtained maximum accuracy equal to 86.21 % and 99.91 % respectively. In conclusion, our work showed promising results for the classification of purposeful and non-purposeful movements.
k-Collective Influential Facility Placement Over Moving Object

In this paper we propose and study the problem of k-Collective influential facility placement over moving object. Specifically, given a set of candidate locations, a group of moving objects, each of which is associated with a collection of reference points, as well as a budget k, we aim to mine a group of k locations, the combination of whom can influence the most number of moving objects. We show that this problem is NP-hard and present a basic hill-climb algorithm, namely GreedyP. We prove this method with (1 - 1/e ) approximation ratio. One core challenge is to identify and reduce the overlap of the influence from different selected locations to maximize the marginal benefits. Therefore, the GreedyP approach may be very costly when the number of moving objects is large. In order to address the problem, we also propose another GreedyPS algorithm based on FM-sketch technique, which maps the moving objects to bitmaps such that the marginal benefit can be easily observed through bit-wise operations. Through this way, we are able to save more than a half running time while preserving the result quality. Experiments on real datasets verify the efficiency and effectiveness for both algorithms we propose in this paper.
Stress Pattern Recognition Through Wearable Biosensors in the Workplace: Experimental Longitudinal Study on the Role of Motion Intensity

Stress is a current issue in the workplace, manifesting itself through both psychological and physiological reactions. Biosensors might improve stress monitoring in the workplace, when employees become wearable device users. Yet, it remains unclear how to identify stress patterns through biosensors without direct observation of the users' activities. In particular, non-physiological aspects of employee activities altering physiological reactions, such as motion activity, may also be associated with stress measures. This longitudinal experimental study examines remote stress identification by testing whether a non-physiological signal of physical activity may improve the classification of stress-related physiological data collected through biosensors. The participants are 18 employees from Public Administration sector wearing biometric devices for around two months in the workplace. This study investigates the stress-related data classification, using established physiological measures (Galvanic Skin Response and Heart Rate) combined with a new non-physiological measure, associated with the user's physical activity (Motion Activity). Stress-related patterns are explored through unsupervised learning approach with help of Gaussian Mixture Model and K-Means classification analysis, completed by the bootstrap confidence intervals for evaluating uncertainty of classification. The results demonstrate that complementing physiological signals with a non-physiological signal, such as a physical activity-related information, improves stress pattern recognition through detection of emotional overarousal, arousal, and relaxation. These findings are especially promising in the context of the use of wearable devices for stress management, when stress-monitoring is done remotely and user' activity is not directly observed during measurements. Further research and cross-validation procedures should be used for building stress-identification algorithms for remote stress monitoring that include physiological and non-physiological signals. Better understanding of stress measures may enhance the quality of stress management data collection processes through Information Systems, involved in the use of wearable devices in the workplace, and strengthen the data governance.
Autonomous Generation of Observation Schedules for Tracking Satellites with Structured-Chromosome GA Optimisation

This paper addresses the problem of autonomous scheduling of space objects’ observations from a network of tracking stations to enhance the knowledge of their orbit while respecting allocated resources. This task requires the coupling of a state estimation routine and an optimisation algorithm. As for the former, a sequential filtering approach to estimate the satellite state distribution conditional on received indirect measurements has been employed. To generate candidates, i.e. observation campaigns, a Structured-Chromosome Genetic Algorithm optimiser has been developed, which is able to address the issue of handling mixed-discrete global optimisation problems with variable-size design space. The search algorithm bases its strategy on revised genetic operators that have been reformulated for handling hierarchical search spaces. The presented approach aims at supporting the space sector by tracking both operational satellites and non-collaborative space debris in response to the challenge of a constantly increasing population size in the near Earth environment. The potential of the presented methodology is shown by solving the optimisation of a tracking window schedule for a very low Earth satellite operating in a highly perturbed dynamical environment.
Global Sensitivity Analysis Using Augmented Radial Basis Function

Global sensitivity analysis (GSA) is a crucial technique for quantifying the uncertainty of input variables on the response of analysis models, which can be utilized to decouple the independent variables or fix the unessential input parameters in the optimization process. When the time consuming model are simulated to get the system response, the traditional Monte Carlo based GSA is hard to be implemented, and the surrogate based approach is an excellent alternative for GSA. In this research, an efficient sensitivity analysis method is developed based on the augmented radial basis function (A-RBF) metamodel. To this end, the orthogonal linear items is introduced to in the construction augmented RBF metamodel. Then the Sobol’ indices of the modified A-RBF metamodel is derived analytically by converting the high-dimensional integrals into the products of one-dimensional integrals and the computational cost is accordingly reduced. Several test cases are performed to validate the efficiency of the proposed GSA method and the results show that the proposed method is suitable for the GSA of time consuming modles.
Time Delay Estimation Based on Backtracking Gradient Descent

In delay estimation of wireless positioning under less samples (single snapshot) and low signal-to-noise ratio (SNR) condition, the use of sparse reconstruction method can improve the estimation accuracy. However, the existing reconstruction algorithms have wrong and missed election in the choice of atoms, resulting in the limitation of final estimation accuracy. In view of this problem, this paper considers the Orthogonal Frequency Division Multiplexing (OFDM) communication system and proposes a novel delay estimation algorithm based on Backtracking Gradient Descent (BGD). Firstly, the algorithm establishes a sparse representation model of channel impulse response. Secondly, it proves that the sensing matrix satisfies the Restricted Isometry Property (RIP). Thirdly, the minimization of the inequality constraint 0 l norm is rewritten as the 2 l norm and the Gradient Descent (GD) algorithm is derived. Then, the backtracking mechanism is introduced and the BGD algorithm is proposed. Finally, it obtains the unbiased delay estimation with the one by one correspondence between the delay value and the complex fading coefficient of sparse coefficient vector. Theoretical analysis and simulation results show that the proposed algorithm has much higher estimation accuracy than the Root-Music algorithm. At the same time, compared with the GD algorithm, the performance is greatly improved at the expense of little complexity increase.
Volatile Species in Comet 67P/Churyumov-Gerasimenko: Investigating the Link from the ISM to the Terrestrial Planets

Comets contain abundant amounts of organic and inorganic species. Many of the volatile molecules in comets have also been observed in the interstellar medium and some of them even with similar relative abundances, indicating formation under similar conditions or even sharing a common chemical pathway. There is a growing amount of evidence that suggests comets inherit and preserve substantial fractions of materials inherited from previous evolutionary phases, potentially indicating that commonplace processes occurred throughout comet-forming regions. Through impacts, part of this material has also been transported to the inner planetary system, including the terrestrial planets. While comets have been ruled out as a major contributor to terrestrial ocean water, substantial delivery of volatile species to the Earth's atmosphere, and as a consequence also organic molecules to its biomass, appears more likely. Comets contain many species of pre-biotic relevance and molecules that are related to biological processes on Earth, and have hence been proposed as potential indicators for the presence of biological processes in the search of extraterrestrial life. While the delivery of cometary material to Earth may have played a crucial role in the emergence of life, the presence of such alleged biosignature molecules in the abiotical environment of comets complicates the detection of life elsewhere in the universe.
Hyperluminous starburst gives up its secrets

HATLAS J084933.4 + 021443 was identified as a dusty starburst via its rest-frame far-infrared (far-IR) emission. Multifrequency imaging and spectroscopy revealed a cluster of four dusty galaxies at z = 2.41, covering 80 kpc. Here, we use Atacama Large Millimetre Array to confirm a more distant, fifth protocluster member, and present X-ray and rest-frame optical imaging spectroscopy of the brightest, an unlensed hyperluminous IR galaxy (HyLIRG). The data reveal broad Hα and bright [N ii] lines, and bright X-ray emission, characteristics that betray a Type-1 active galactic nucleus (AGN), strengthening evidence that AGN are ubiquitous amongst HyLIRGs. The accreting black hole is supermassive, Mbh ≈ 2 × 109 M⊙, with little intrinsic absorption, NH ≈ 5 × 1021 cm−2. The X-ray properties suggest the accretion luminosity rivals that of the starburst, yet it is not obvious where this emerges in its panchromatic spectral energy distribution. We outline three scenarios that could give rise to the observed characteristics, and how we might distinguish between them. In the first, we see the AGN through the host galaxy because of the cavity it excavates. In the others, the AGN is not cospatial with the starburst, having been ejected via asymmetric gravitational radiation, or having evolved towards the naked quasar phase in an unseen companion.