Miracle-dz/UniFAR-FTUs huggingface dataset:
WIP: Collaborative Approaches to Mitigate Links of Variable Quality in LoRa Networks

LoRa has become a de-facto standard for long-range communication networks with low data rate requirements. Despite the robustness of its employed chirp spread spectrum modulation, however, LoRa links covering long distances can be prone to errors and losses. A common reaction to such link conditions is to increase the symbol durations and thus their chance of a correct reception. In this paper, we follow an alternative approach to mitigate lossy links by adding redundant information to LoRa transmissions. We investigate the potentials of both local and collaborative methods to increase the network’s overall data delivery. By means of simulation studies and a practical evaluation, for which we have implemented the designed techniques on actual LoRa hardware devices, we show that significant improvements are possible in a real-world deployment.
A Diffusion FXLMS Algorithm for Multi-Channel Active Noise Control and Variable Spatial Smoothing

This paper studies the diffusion (Diff) control for multichannel ANC systems, where a group of controllers and error microphones are physically distributed at different locations within a large area. In this case, the conventional consensus agreement for controllers cannot be reached. To solve this problem, a new Diff filtered-x least mean squares (Diff-FxLMS) algorithm that incorporates the knowledge of spatial smoothness is proposed. Compared to conventional Diff control criteria that have a fixed spatial smoothing strategy, the proposed Diff-FxLMS adjusts a so called spatial regularization (SR) coefficient adaptively such that the neighboring controllers keep their decision variables close to one another to minimize the global cost function while giving preference to possibly distinct local signals. A detailed performance analysis is carried out and verified by simulation. Based on the analysis, a variable SR formula is derived. The performance of the proposed algorithm is also compared with conventional methods.
Sparse Parameter Estimation for PMCW MIMO Radar Using Few-Bit ADCs

In this work, we consider target parameter estimation of phase-modulated continuous-wave (PMCW) multiple-input multiple-output (MIMO) radars with few-bit analog-to-digital converters (ADCs). We formulate the estimation problem as a sparse signal recovery problem and modify the fast iterative shrinkage-thresholding algorithm (FISTA) to solve it. The ℓ2,1-norm is adopted to promote the sparsity in the range-Doppler-angle domain. Simulation results show that using few-bit ADCs can achieve comparable performance to many-bit ADCs when targets are widely separated. However, if targets are spaced closely, performance losses can occur when 1-bit ADCs are applied.
New Variants of DFA Based on Loess and Lowess Methods: Generalization of the Detrending Moving Average

Proposed early in the 90ies, the detrended fluctuation analysis (DFA) can be used to estimate the Hurst exponent and has been proven relevant in various applications, from economics to biomedical. For the last years, variants have been proposed. They differ in the way to estimate the trend of the centered integrated signal. In this paper, we recall the main principles of some of these methods, provide explanations on the behaviours of the algorithms and analyze the relevance of new variants based on the Savitzky-Golay filter, also known as the LOESS approach, and the LOWESS. They bridge the gap between the DFA and the detrending moving average (DMA). We hence show that the LOESS-based method is a generalization of the DMA.
A Real-Time Bridge Crack Detection Method Based on an Improved Inception-Resnet-v2 Structure

Bridge crack detection is essential to ensure bridge safety. The introduction of deep learning technology has made it possible to detect bridge cracks automatically and accurately. In this study, the Inception-Resnet-v2 algorithm was systematically improved and applied to the real-time detection of bridge cracks. We propose an end-to-end bridge crack detection model based on a convolutional neural network. This model combines the advantages of Inception convolution and residual networks, broadening the network width and alleviating the training problem of the deep network. The calculation speed is improved while still ensuring accuracy. Multi-scale feature fusion enables the network to extract contextual information of different scales, which improves the accuracy of crack recognition. The GKA (K-means clustering method based on a genetic algorithm) realizes the accurate segmentation of the target area, greatly enhances the clustering effect, and effectively improves the detection speed. In this model, large fracture datasets are used for training and testing without pre-training. The experimental results show that the performance of this method was improved in all aspects: accuracy, 99.24%; recall, 99.03%; F-measure, 98.79%; and FPS(Frames Per Second), 196.
GC/MS ANALYSIS OF CS_2-EXTRACTS FROM SEVERAL BITUMINOUS COALS

The CS 2 soluble fractions from Pingshuo, Datong and Shenfu bituminous coals were analyzed using GC/MS and their chemical constituents and structural characteristic were studied. The results show that the CS 2 extracts mainly consist of arenes, aliphatic hydrocarbons and heteroatom-containing compounds. Arenes are dominant components among which benzene, naphthalene, phenanthrene and pyrene ring-contaning compounds with and without alkyl substitutes are main components. Aliphatic hydrocarbons primarily consist of n-alkanes with small amounts of acyclic isoprenoids and terpanes. Only very small amounts of heteroatom-containing compounds were detected, among those oxygen-, nitrogen- and sulfur-containing compounds are main components. Small amounts of o-dichlorobenzene were found in the CS 2 soluble fraction from Pingshuo and Datong coals.
The in-the-Wild Speech Medical Corpus

Automatic detection of speech affecting (SA) diseases has received significant attention, particularly in clinical scenarios. However, the same task in in-the-wild conditions is often neglected, in part, due to the lack of appropriate datasets.In this work, we present the in-the-Wild Speech Medical (WSM) Corpus, a collection of in-the-wild videos, featuring subjects potentially affected by a SA disease - specifically, depression or Parkinson’s disease. The WSM Corpus contains a total 928 videos, and over 131 hours of speech. Each video is accompanied by a crowdsourced annotation for perceived age/gender, and self-reported health status of the speaker. The WSM Corpus is balanced over all the labels.In this work we present a detailed description of the collection, and annotation processes of the WSM corpus. Furthermore, we present present several baseline systems for the detection of SA diseases using speech alone, thus motivating the use of this type of in-the-wild data in paralinguistic audiovisual tasks.
Hubert: How Much Can a Bad Teacher Benefit ASR Pre-Training?

Compared to vision and language applications, self-supervised pre-training approaches for ASR are challenged by three unique problems: (1) There are multiple sound units in each input utterance, (2) With audio-only pre-training, there is no lexicon of sound units, and (3) Sound units have variable lengths with no explicit segmentation. In this paper, we propose the Hidden-Unit BERT (HUBERT) model which utilizes a cheap k-means clustering step to provide aligned target labels for pre-training of a BERT model. A key ingredient of our approach is applying the predictive loss over the masked regions only. This allows the pre-training stage to benefit from the consistency of the unsupervised teacher rather that its intrinsic quality. Starting with a simple k-means teacher of 100 cluster, and using two iterations of clustering, the HUBERT model matches the state-of-the-art wav2vec 2.0 performance on the ultra low-resource Libri-light 10h, 1h, 10min supervised subsets.
Context-Aware Speech Stress Detection in Hospital Workers Using Bi-LSTM Classifiers

Hospital workers are known to work long hours in a highly stressful environment. The COVID-19 pandemic has increased this burden multi-fold. Pre-COVID statistics already showed that one in every three nurses reported burnout, thus affecting patient satisfaction and the quality of their provided service. Real-time monitoring of burnout, and other underlying factors, such as stress, could provide feedback not only to the clinical staff, but also to hospital administrators, thus allowing for supportive measures to be taken early. In this paper, we present a context-aware speech-based system for stress detection. We consider data from 144 hospital workers who were monitored during their daily shifts over a 10-week period; subjective stress readings were collected daily. Wearable devices measured speech features and physiological readings, such as heart rate. Environment sensors, in turn, were used to track staff movement within the hospital. Here, we show the importance of context-awareness for stress level detection based on a bidirectional LSTM deep neural network. In particular, we show the importance of hospital location and circadian rhythm based contextual cues for stress prediction. Overall, we show improvements as high as 14% in F1 scores once context is incorporated, relative to using the speech features alone.
Blind and Neural Network-Guided Convolutional Beamformer for Joint Denoising, Dereverberation, and Source Separation

This paper proposes an approach for optimizing a Convolutional BeamFormer (CBF) that can jointly perform denoising (DN), dereverberation (DR), and source separation (SS). First, we develop a blind CBF optimization algorithm that requires no prior information on the sources or the room acoustics, by extending a conventional joint DR and SS method. For making the optimization computationally tractable, we incorporate two techniques into the approach: the Source-Wise Factorization (SW-Fact) of a CBF and the Independent Vector Extraction (IVE). To further improve the performance, we develop a method that integrates a neural network (NN) based source power spectra estimation with CBF optimization by an inverse-Gamma prior. Experiments using noisy reverberant mixtures reveal that our proposed method with both blind and NN-guided scenarios greatly outperforms the conventional state-of-the-art NN-supported mask-based CBF in terms of the improvement in automatic speech recognition and signal distortion reduction performance.
A Two-Stage Deep Modeling Approach to Articulatory Inversion

This paper proposes a two-stage deep feed-forward neural network (DNN) to tackle the acoustic-to-articulatory inversion (AAI) problem. DNNs are a viable solution for the AAI task, but the temporal continuity of the estimated articulatory values has not been exploited properly when a DNN is employed. In this work, we propose to address the lack of any temporal constraints while enforcing a parameter-parsimonious solution by deploying a two-stage solution based only on DNNs: (i) Articulatory trajectories are estimated in a first stage using DNN, and (ii) a temporal window of the estimated trajectories is used in a follow-up DNN stage as a refinement. The first stage estimation could be thought of as an auxiliary additional information that poses some constraints on the inversion process. Experimental evidence demonstrates an average error reduction of 7.51% in terms of RMSE compared to the baseline, and an improvement of 2.39% with respect to Pearson correlation is also attained. Finally, we should point out that AAI is still a highly challenging problem, mainly due to the non-linearity of the acoustic-to-articulatory and one-to-many mapping. It is thus promising that a significant improvement was attained with our simple yet elegant solution.
ADMM-Based ML Decoding: from Theory to Practice

Integer Linear Programming (ILP) is a general method to solve the Maximum-Likelihood (ML) decoding problem for all kinds of binary linear codes. To this end, state-of-the-art techniques use a Branch-and-Bound (B&B) framework to partition the underlying integer linear problem into several relaxed linear problems. These linear problems then have to be solved in reasonable time by an efficient Linear Programming (LP) solver. Recently, the Alternating Direction Method of Multipliers (ADMM) has been proposed for efficient software and hardware LP decoding of sparse codes, hence, an ADMM-based ML decoder seems to be a promising approach. In this paper, we investigate this approach with respect to its algorithmic and implementation-specific challenges.
Radar Clutter Classification Using Expectation-Maximization Method

In this paper, the problem of classifying radar clutter returns into statistically homogeneous subsets is addressed. To this end, latent variables, which represent the classes to which the tested range cells belong, in conjunction with the expectation- maximization method are jointly exploited to devise the classification architecture. Moreover, two different models for the structure of the clutter covariance matrix are considered. At the analysis stage, numerical examples based on simulated data for the classification performance are presented showing the effectiveness of the proposed classification schemes.
Evaluating and comparing memory error vulnerability detectors

Objective: Our study aims to assess and explain the strengths and weaknesses of state-of-the-art memory error vulnerability detectors based on static and/or dynamic code analysis, so as to inform tool selection by practitioners and future design of better detectors by researchers and tool developers. Method: We empirically evaluated and compared ﬁve state-of-the-art memory error vulnerability detectors against two benchmark datasets of 520 and 474 C/C++ programs, respectively. We conducted case studies to gain in-depth explanations of successes and failures of individual tools. Results: While generally fast, these detectors had largely varied accuracy across diﬀerent vulnerability categories and moderate overall accuracy. Complex code (e
A New Asymmetric Cascaded Multilevel Converter Topology With Reduced Voltage Stress and Number of Switches

This paper presents a new structure of a multilevel inverter with fewer components, which is suitable for renewable energy sources and industrial loads applications. The structure has three unequal input sources and ten switches that can generate a 15-level output voltage. Furthermore, it can be connected in cascade for increasing, even more, the number of levels and output voltage. The main feature of the proposed inverter is its very low harmonic distortion at the output voltage and current due to the control method, which is based on the nearest level control method for generating a high-quality output voltage. A typical application of this inverter is in solar cells and wind turbines. Both simulations in Matlab/Simulink and experimental results in a scaled-down prototype validate the proposed theoretical analysis.
Fundamental Trade-Offs in Noisy Super-Resolution with Synthetic Apertures

This paper concerns understanding the fundamental performance benefits of co-array based super-resolution techniques in low SNR settings. Proper sensor placement in the form of a sparse nested array allows the generation of difference co-arrays with large virtual apertures that offer higher resolution. However, the performance of super-resolution aperture synthesis techniques is known to degrade in presence of large noise levels. The main contribution of this paper is to rigorously establish that nested arrays provide lower Cramér-Rao bounds than a ULA (with the same number of sensors) in the low SNR regime, and therefore can lead to better resolvability of closely spaced sources. Numerical experiments are performed to validate theoretical claims, including demonstration of MUSIC spectra with two closely-spaced sources using ULA and nested arrays in various SNR settings.1
Brief Industry Paper: Catching IoT Malware in the Wild Using HoneyIoT

Constantly increasing botnets powered by vulnerable IoT devices perform record-breaking DDoS attacks to critical infrastructures. Therefore, it is imperative to find vulnerabilities in IoT devices ahead of attackers. In this paper, we first present a systematic analysis on various kinds of IoT malware to further explore the challenges of IoT Honeypot design. We then propose HoneyIoT, a scalable IoT Honeypot framework, which aims at attracting IoT attacks and recording malicious behaviors with configurable vulnerabilities and firmware support. During a 7day real-world industrial experiment, HoneyIoT observed over 12,500 malicious connections and 3,423 distinct login attempts.
Joint User Pairing and Power Allocation for NOMA-Based GEO and LEO Satellite Network

Multi-layer satellite networks (MLSNs) is of great potential for the integrated 5G networks to provide diversified services. However, MLSNs confront frequency interference coordination problem between satellite systems in different orbits. This paper investigates a joint user pairing and power allocation scheme in a non-orthogonal multiple access (NOMA)-based geostationary earth orbit (GEO) and low earth orbit (LEO) satellite network. Specifically, a novel NOMA framework with two uplink receivers, i.e. the GEO and LEO satellites is established where the NOMA groups are formed considering the subcarrier assignment of ground users. To maximize the system capacity, an optimization problem is then introduced subject to the decoding threshold and power consumption. Since the formulated problem is non-convex and mathematically intractable, we decompose it into user pairing and power allocation schemes. In the user pairing scheme, virtual GEO users are generated to transform the multi-user pairing problem into a matching problem and a max-min pairing strategy is adopted to ensure the fairness among NOMA groups. In the power allocation scheme, the non-convex problem is transformed into multiple convex subproblems and solved by iterative algorithm. Simulation results validate the effectiveness and superiority of the proposed schemes when compared with several existing schemes.
Tightening Network Calculus Delay Bounds by Predicting Flow Prolongations in the FIFO Analysis

Network calculus offers the means to compute worst-case traversal times based on interpreting a system as a queueing network. A major strength of network calculus is its strict separation of modeling and analysis frameworks. That is, a model is purely descriptive and can be put into multiple different analyses to derive a data flow’s worst-case traversal time bound. One of the recent results in this category is the so-called flow prolongation. Flow prolongation actively manipulates the internal model of the analysis by virtually extending the path of flows, i.e., by deliberately creating a more pessimistic setting of resource contention between flows. It was shown that flow prolongation can theoretically decrease worst-case traversal time bounds under certain assumptions. Yet, due to its exhaustive search, it was also shown that flow prolongation does not scale and it might not even have an impact in larger queueing networks. In this paper we introduce DeepFP, an approach to make the analysis scale by predicting flow prolongations using a graph neural network. In our evaluation, we show that DeepFP can improve results in networks of FIFO queues considerably, where the delay bound can be reduced by 13.7% in large FIFO networks at negligible additional cost on the execution time of the analysis.
Speech Emotion Recognition Based on Listener Adaptive Models

This paper presents a novel speech emotion recognition scheme that can deal with the individuality of emotion perception. Most conventional methods directly model the majority decision of multiple listener’s perceived emotions. However, emotion perception varies with the listener, which means the conventional methods can mismatch the recognition results to human perception. In order to mitigate this problem, we propose a Listener Adaptive (LA) model that reflects emotion recognition criteria of each listener. One-hot listener codes with several adaptation layers are employed in the LA model. The LA model yields the posterior probabilities of the listener-specific perceived emotions. Majority-voted emotion can be also estimated by averaging, in the LA model, the posterior probabilities for all listeners. Experiments on two emotional speech datasets demonstrate that the proposed approach offers improved listener-wise perceived emotion recognition performance in natural speech.
A Secured Advanced Management Architecture in Peer-to-Peer Energy Trading for Multi-Microgrid in the Stochastic Environment

Careful consideration of grid developments illustrates the fundamental changes in its structure which its developments have taken place gradually for a long time. One of the most important developments is the expansion of the communication infrastructure that brings many advantages in the cyber layer of the system. The actual execution of the peer-to-peer (P2P) energy trading is one core advantage which also may lead to the systematic risks such as cyber-attacks. Consequently, it is necessary to form a useful way to cover such challenges. This paper focuses on the online detection of false data injection attack (FDIA), which tries to disrupt the trend of optimal peer-to-peer energy trading in the stochastic condition. Moreover, this article proposes an effective modified Intelligent Priority Selection based Reinforcement Learning (IPS-RL) method to detect and stop the malicious attacks in the shortest time for effective energy trading based on the peer to peer structure. The presented method is compared with other methods such as support vector machine (SVM), reinforcement learning (RL), particle swarm optimization (PSO)-RL, and genetic algorithm (GA)-RL to validate the functionality of the method. The proposed method is implemented and examined on three interconnected microgrids in the form of peer-to-peer structure wherein each microgrid has various agents such as photovoltaic (PV), wind turbine, fuel cell, tidal system, storage unit, etc. Eventually, the unscented transformation (UT) is applied for uncertainty analysis and making the near-reality simulations.
Enhanced Off-Policy Reinforcement Learning With Focused Experience Replay

Utilizing the collected experience tuples in the replay buffer (RB) is the primary way of exploiting the experiences in the off-policy reinforcement learning (RL) algorithms, and, therefore, the sampling scheme for the experience tuples in the RB can be critical for experience utilization. In this paper, it is found that a widely used sampling scheme in the off-policy RL suffers from inefficiency due to the inadequate uneven sampling of experience tuples from the RB. In fact, the conventional uniform sampling of the experience tuples in the RB causes a severely unbalanced experience utilization, since experiences stored earlier in the RB is sampled with much higher frequency especially in the early stage of learning. We mitigate this fundamental problem by employing a half-normal sampling probability window that allocates a higher sampling probability to newer experiences in the RB. In addition, we propose general and local size adjustment schemes that determine the standard deviation of the half-normal sampling window to enhance the learning speed and performance and to mitigate the temporary performance degradation during training, respectively. For performance demonstration, we apply the proposed sampling technique to the state-of-the-art off-policy RL algorithms and test for various RL benchmark tasks such as MuJoCo gym and CARLA simulator. As a result, the proposed technique shows considerable learning speed and final performance improvement, especially on the tasks with large state and action space. Furthermore, the proposed sampling technique increases the stability of the considered RL algorithms, verified with less variance of the performance results across different random seeds of network initialization.