Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Weather-Aware Prediction of User Activities and Future Visits

In recent years, <italic>Location-Based Social Networking</italic> (LBSN) sites such as <italic>Foursquare</italic>, <italic>Facebook Places</italic>, and <italic>Twitter</italic> have become extremely popular due to the extensive usage of location-enabled smart phone technologies. These LBSNs allow users to post their <italic>check-ins</italic> that provide important set of information about users’ activities and preferences. Several existing research works predict users’ activities from social media <italic>check-ins</italic> data considering various aspects such as <italic>time</italic>, <italic>venue</italic>, and <italic>occurrences</italic>. However, none of the earlier studies investigate the influence of weather on users’ preferences of <italic>activities</italic> and <italic>mode of transportation preferences</italic>. Psychological studies show that weather has a strong influence on human activities. In this paper, we predict users’ <italic>travel mode</italic>, <italic>day/night time activities</italic> and <italic>future visit</italic> from weather condition derived from social media <italic>check-ins</italic>. In particular, we develop several classification models to predict users’ preferable mode of transportation, day/night activities, and future visits from the users’ <italic>check-ins</italic> based on different weather conditions. We use two real datasets: <italic>Tokyo</italic> and <italic>New York city</italic> to validate our models. Our classifiers achieve substantial strength (on an average AUC of 72.77%) to predict users’ <italic>mode of transportation, day/night activities</italic> and their <italic>future visit preferences</italic> for Tokyo dataset. We also compare performance of the classifiers developed by these two datasets.
Intelligent Diagnosis for Railway Wheel Flat Using Frequency-Domain Gramian Angular Field and Transfer Learning Network

The intelligent diagnosis of wheel flat based on vibration image classification is a promising research subject for performance maintenance of railway vehicles. However, the image representation method of vibration signal and classification network construction under small samples have become two obstacles to intelligent diagnosis of wheel flat. This paper presents a novel frequency-domain Gramian angular field (FDGAF) algorithm to encode the vibration signal of wheel flat to featured images. Furthermore, a modified transfer learning network is introduced to classify these featured images under small samples without any involvement of prior knowledge. The proposed FDGAF can calculate the Gramian angular matrix of axle box acceleration signal in frequency domain and assign frequency position dependence to the featured images to preserve original characteristic information. Then, these featured images can be intelligent classified by a transfer learning network under the condition of 30 sample without require of prior knowledge. To verify the efficiency of this proposed method, 12 cases of artificial wheel flats are processed on a scaled railway test rig, and their axle box acceleration signals are collected to obtain visual diagnosis results. The verfication proves that FDGAF is able to obtain accurate diagnostic results with high separability, for separability indexes of FDGAF reaches 10.8, 8.7, 14.9, and 5.8. We anticipate that this method will find use in the performance maintenance of railway vehicles and the improvement of industrial condition monitoring.
EASpiNN: Effective Automated Spiking Neural Network Evaluation on FPGA

Neural networks (NNs) have been widely used in many machine learning algorithms and have been deployed for various industrial applications like image classification, speech recognition, and automated control. Spiking neural network (SNN), known as the third-generation neural network, incorporates timing information in the network and is more biologically plausible [1]. Compared to today's artificial and convolutional neural networks (ANN and CNN) where all neurons in each layer will always be activated and computed, SNN only activates those neurons whose membrane potential exceed the threshold potential [2]. As a result, SNN requires fewer computation resources and less data communication between network layers due to its event-driven nature. Although SNN has been blamed for the relatively lower accuracy, recent studies on converted SNNs have improved its accuracy to a similar level of ANN and CNN for smaller network models like MNIST and CIFAR-10, and have demonstrated the great potential of SNN in future deep learning systems [2].
Differential Detection of Facial Retouching: A Multi-Biometric Approach

Facial retouching apps have become common tools which are frequently applied to improve one’s facial appearance, e.g. before sharing face images via social media. Beautification induced by retouching has the ability to substantially alter the appearance of face images and hence might represent a challenge for face recognition. Towards deploying secure face recognition as well as enforcing anti-photoshop legislations, a robust and reliable detection of retouched face image is needed. Published approaches consider a single image-based (no-reference) scenario where a potentially retouched face image serves as sole input to the retouching detector. However, in many cases a trusted unaltered face image of a subject examined is available which enables an image pair-based (differential) detection scheme. In this work, ICAO-compliant subsets of the FERET and FRGCv2 face databases are used to automatically create a database containing 9,078 retouched face images together with unconstrained probe images. In evaluations employing the commercial Cognitec FaceVACS and the open-source ArcFace face recognition system, it is shown that facial retouching can negatively impact face recognition performance. Further, a differential facial retouching detection system is proposed which processes pairs of a potentially retouched reference image and corresponding unaltered probe image of single subjects. Estimated differences in feature vectors obtained from texture descriptors, facial landmarks, and deep face representations are leveraged by machine learning-based classifiers of which the detection scores are fused to distinguish between retouched and unaltered face images. The proposed scheme is evaluated in a cross-database scenario where training and testing are performed on the FERET and FRGCv2 databases and vice versa. In the scenario where the used retouching algorithm is known by the detection algorithm, a competitive average D-EER of approximately 2% is achieved. Further, the scenario in which the employed retouching algorithm is not known by the detection algorithm is evaluated. In the latter scenario, the proposed approach obtains an average D-EER below 10% and is shown to outperform several state-of-the-art single image-based detection schemes.
Row-Stochastic Matrices Based Distributed Optimization Algorithm With Uncoordinated Step-Sizes

This paper investigates the distributed optimization problem over multi-agent networks, in which the target of agents is to collaboratively optimize the sum of all local objective functions. Each local objective function is uniquely known by a single agent. We concentrate on the scenario where communication among agents is portrayed as directed graphs. Based on the exact first order method, a fully distributed optimization algorithm is proposed to deal with the optimization problem. The proposed algorithm utilizes row-stochastic matrices and uncoordinated step-sizes, which exactly drives all agents to converge to the global optimization solution. Under the assumptions that the global objective function is strong convex and the local objective functions have Lipschitz continuous gradient, we show that the proposed algorithm linearly converges to the global optimization solution as long as the maximum step-size of agents does not exceed an explicitly characterized upper bound. Finally, numerical experiments are presented to demonstrate the correctness of theoretical analysis.
Contaminated effect of Geomagnetic storm on pre-seismic atmospheric and ionospheric anomalies during Imphal earthquake.

Understanding of Seismo-ionospheric coupling mechanism requires a quiet geomagneticcondition to eliminate any kind of contamination in the lower atmospheric and ionospheric parameters. We present the evidence of such event during a strong earthquake happened in Imphal on January 4, 2016 (M=6.7). We studied the lower ionospheric irregularities using Very Low Frequency radio wave characteristics as observed from fixed frequency transmitter JJI (22.2 kHz) in Japan by using the conventional nighttime fluctuation methods. We also studied the presence of Atmospheric Gravity Wave (AGW) in VLF data and also computed from SABER/TIMED satellite observation. Two geomagnetic storms occurred on December 21, 2015 and January 1, 2016. By the conventional analysis, we found there are significant decrease in nighttime trend and increase in nighttime fluctuations, but seismo-genic effects are quite contaminated due to the storm. The AGW variation shows similar effects but less prominent. By analysis of SABER data, we found enhancement of AGW, but effects are rather post-storm effect instead of pre-seismic. The same results obtained when the presence of wavelike structure are computed from the Fast Fourier Transform of the nighttime VLF data. So, this paper shows a significant example of elimination of contamination of pre-seismic phenomena from other terrestrial solar energetic sources.
Transfer Learning from Audio Deep Learning Models for Micro-Doppler Activity Recognition

This paper presents a mechanism to transform radio frequency micro-Doppler signatures into a pseudo-audio representation, which results in significant improvements in transfer learning from a deep learning model trained on audio. We also demonstrate that transfer learning from a deep learning model trained on audio is more effective than transfer learning from a model trained on images, which suggests machine learning methods used to analyse audio can be leveraged for micro-Doppler. Finally, we utilise an occlusion method to gain an insight into how the deep learning model interprets the micro-Doppler signatures and the subsequent pseudo-audio representations.
NLOS Detection using UWB Channel Impulse Responses and Convolutional Neural Networks

Indoor environments often pose challenges to RFbased positioning systems. Typically, objects within the environment influence the signal propagation due to absorption, reflection, and scattering effects. This results in errors in the estimation of the time or arrival (TOA) and hence leads to errors in the position estimation. Recently, different approaches based on classical, feature-based machine learning (ML) have successfully detected such obstructions based on CIRs of ultra wideband (UWB) positioning systems.This paper applies different convolutional neural network architectures (ResNet, Encoder, FCN) to detect non line-of-sight (NLOS) channel conditions directly from the CIR raw data. A realistic measurement campaign is used to train and evaluate the algorithms. The proposed methods highly outperform the featurebased ML baselines while still using low network complexities. We also show that the models generalize well to unknown receivers and environments and that positioning filters benefit significantly from the identification of NLOS measurements.
Assessing Block-Sparsity-Based Spectrum Sensing Approaches for Cognitive Radar on Measured Data

Due to increasing demands for spectral resources in both communication and radar systems, the Radio Frequency (RF) electromagnetic spectrum is becoming more and more crowded with interfering nuisances. In order to tackle the scarcity of available spectral intervals, in recent years a multitude of spectrum sensing algorithms have been developed for improving spectrum sharing. Among these, two-dimensional (2-D) spectrum sensing can be used to obtain real time space-frequency electromagnetic spectrum awareness. Specifically, this approach makes it possible to optimize the spectrum usage of certain spectrum portions whose occupancy varies both temporally and spatially. In this paper, we evaluate the effectiveness of certain space-frequency map recovery algorithms relying on the use of commercially-available hardware. To this end, we employ an inexpensive four channel coherent receiver using Software Defined Radio (SDR) components for emitter localization. Hence, after proper calibration of the receiving system, the acquired samples are used to evaluate the effectiveness of different signal processing strategies which exploit the inherent block-sparsity of the overall profile. At the analysis stage, results reveal the effectiveness of such algorithms.
Sparse Range-Doppler Image Construction with Neural Networks

The principles outlined by compressed sensing can permit a sensor to collect reduced amount of data and still reconstruct an exact outcome. This can for example be used to generate super-resolution sparse range-Doppler radar images while emitting a reduced number of pulses within a coherent processing interval. In this paper, we investigate the use of neural networks as a mean to solve the sparse reconstruction problem with specific emphasis towards range-Doppler images. The neural networks are trained to generate a sparse Doppler profile from incomplete time domain data in line with traditional sparse $L_{1}$-norm minimization. We show that this approach is viable through fully connected feed forwarding networks and the results closely mimic sparse recovered range-Doppler maps.
Human Position Guidance Using Vibrotactile Feedback Stimulation Based on Phantom-Sensation

In this paper, we propose a novel method to guide a person around space using continuous vibrotactile feedback on the wrist. Different from previous research, we use the Phantom Sensation illusion to produce “virtual” cues in places where there is no physical vibrotactile actuator. This allows us to produce a vibration at any place of the skin around the wrist. Users are requested to follow the produced cues in order to move around space by moving towards the direction signaled by the cue (“pull” mapping). We also propose using the vibration amplitude in order to represent the proximity to the goal. Through experiments, we evidenced that users were able to reach arbitrary positions around space solely guided by the vibrotactile cues. Each user performed the task both with and without the proximity information. Although we did not observe a significant difference in the performance, we discuss the trade-offs about mapping the proximity to the vibration amplitude. We think this method can be applied for intelligent indoor navigation or group coordination, and we hope to expand the concept further to simultaneously guide multiple people moving at higher speed, such as team sports coaching.
Exploring The Impact Of Switch Arity On Butterfly Fat Tree Fpga Nocs

Overlay Networks-on-Chip (NoCs) for FPGAs based on the Butterfly-Fat Tree (BFT) topology with lightweight flow control deliver low LUT costs and features such as in-order delivery and livelock freedom. BFT NoCs make it possible to conFigure network bandwidth to match application requirements, by choosing switch types with different numbers of ports (arity) for the layers of the tree hierarchy. We increase the design space of BFT NoC configurations available to designers by constructing networks with larger arity-4 switches, in addition to the arity2 switches explored by previous works. When synthesized for the Xilinx UltraScale + VU9P FPGA, our proposed BFT NoCs consume 38-45% fewer LUTs and 33-50% smaller wiring lengths than arity-2 BFT NoCs with the same Rent parameter, in exchange for a reduction in maximum clock frequency in up to 25%. We simulate the operation of our proposed NoCs when routing various real-world workloads with 64 network clients, and show that they consistently achieve better Throughput / LUT cost ratios, when compared to arity-2 BFT NoCs with the same Rent parameter, with improvements of 15 to 120% depending on the benchmark and NoC topology.
Automotive Radar Interference Mitigation using a Convolutional Autoencoder

Automotive radar interference imposes big challenges on signal processing algorithms as it raises the noise floor and consequently lowers the detection probability. With limited frequency bands and increasing number of sensors per car, avoidance techniques such as frequency hopping or beamforming quickly become insufficient. Detect-and-repair strategies have been studied intensively for the automotive field, to reconstruct the affected signal samples. However depending on the type of interference, reconstruction of the time domain signals is a highly non-trivial task, which can affect following signal processing modules. In this work an autoencoder based convolutional neural network is proposed to perform image based denoising. Interference mitigation is phrased as a denoising task directly on the range-Doppler spectrum. The neural networks shows significant improvement with respect to signal-to-noise-plus-interference ratio in comparison to other state-of-the-art mitigation techniques, while better preserving phase information of the spectrum compared to other techniques.
A Novel Optimized Case-Based Reasoning Approach With K-Means Clustering and Genetic Algorithm for Predicting Multi-Class Workload Characterization in Autonomic Database and Data Warehouse System

Data management systems are essential elements for any organization which is dealing with large volume of data now a days. Due to increase in data volume, and its complexities, it has become more challenging job for workload management system to maintain its performance. So, there is a need of such a system that can autonomically deal with such complexities with less or without human involvement. Performance of these systems can be improved by making the systems well-aware about the workload entering into the system. The workload of a prevalent typical database and data warehouse system can be characterized into three types that is Online Transaction Processing (OLTP), Decision Support Systems (DSS) and Mixed type of workload. Currently, autonomic characterization of workload into a binary class such as OLTP and DSS is being carried out as reported in the literature, however, characterizing the workload into three types that refers to a multi-class classification problem is relatively a more challenging task. In this study, we propose a novel optimized Case-based Reasoning (CBR) approach based on clustering for autonomically characterizing the workload into multi-class types before entering into the system. We implement four phases of CBR along with case-base generation and map it to the elements of autonomic MAPE-K model. In Retrieve phase, k-means clustering is used for enhancing retrieval efficiency and workload types predictions are made in Reuse phase. Genetic Algorithm is used in Revise and Adapt phase of CBR. Few autonomic self_* characteristics are incorporated to make it autonomic. We performed various experiments and results show that the proposed model outperforms in prediction as compared to existing approaches. We performed post-hoc test for the validation of results in comparison with other machine learning classifiers using the Friedman test that show that the proposed model stands out as the best classifier.
An Improved Faster R-CNN for High-Speed Railway Dropper Detection

Overhead contact systems (OCSs) are the power supply facility of high-speed trains and plays a vital role in the operation of high-speed trains. The dropper is an important guarantee for the suspension system of the OCS. Faults of the dropper, such as slack and breakage, can cause a certain threat to the power supply system. How to use artificial intelligence technologies to detect faults is an urgent technical problem to be solved. Because droppers are very small in whole images, a feasible solution to the problem is to identify and locate the droppers first, then segment them, and then identify the fault type of the segmented droppers. This paper proposes an improved Faster R-CNN algorithm that can accurately identify and locate droppers. The innovations of the method consist of two parts. First, a balanced attention feature pyramid network (BA-FPN) is used to predict the detection anchor. Based on the attention mechanism, BA-FPN performs feature fusion on feature maps of different levels of the feature pyramid network to balance the original features of each layer. After that, a center-point rectangle loss (CR Loss) is designed as the bounding box regression loss function of Faster R-CNN. Through a center-point rectangle penalty term, the anchor box quickly moves closer to the ground-truth box during the training process. We validate the improved Faster R-CNN through extensive experiments on the VOC 2012 and MSCOCO 2014 datasets. Experimental results prove the effectiveness of the proposed network combined with attention feature fusion and center-point rectangle loss. On the OCS dataset, the accuracy using the combination of the improved Faster R-CNN and ResNet-101 reached 86.8% mAP@0.5 and 83.9% mAP@0.7, which was the best performance among all results.
Matrix pencil method for DoA estimation with interpolated arrays

The Matrix Pencil Method is combined with techniques for Array Interpolation to create a fast and high-resolution Direction-of-Arrival estimation algorithm for use with arbitrary array geometries. Applicable with only a single snapshot, it is especially suitable in dynamic scenarios, where only an insufficient number of snapshots for subspace methods are available. Simulations show that the proposed Interpolated Matrix Pencil Method outperforms an implementation of Orthogonal Matching Pursuit at moderately high SNR values in terms of metrics such as estimation accuracy and probability of resolution. Feasibility with realistic data is confirmed through measurements collected with an experimental MIMO radar system of optimized sparse topology.
Comparison of the Evaluation Metrics for Neural Grammatical Error Correction With Overcorrection

Grammar error correction (GEC) refers to the proper correction of grammatical errors in a given sentence. Important factors to consider in GEC are not only the grammatical correction of the sentence, but also the recognition of a correct sentence in which no changes are required. However, GEC approaches in which deep learning recently started being used consider only the former aspect, which leads to overcorrection, whereby changes are made to a correct sentence unnecessarily. Because this bias is also reflected in performance metrics, conventional performance metrics consider only part of the important factors in GEC. This study proposes a new metric to consider both important aspects in GEC and to provide a new viewpoint for the GEC task. To the best of the authors knowledge, this study is the first to deal with comprehensively considering the correction performance and overcorrection problem in GEC. The experimental results demonstrate that the model performance ranking was reversed when evaluating the performance with the proposed metric compared to the General Language Understanding Evaluation benchmark [21], which only considers the correction performance. This indicates that the high performance of the correction does not result in less problems with the overcorrection and that the overcorrection problem should also be considered when evaluating the model performance. Moreover, we found that the copy mechanism [14] helps to alleviate the problem of overcorrection.