Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Intrusive Signal Analysis for Room Adaptation of ASR Models

We present a new deep-learning-based non-intrusive signal assessment method (NISA+) that performs a joint estimation of a large set of speech signal parameters, including those related to reverberation (C50, DRR, reflection coefficient and room volume), background noise (SNR), perceptual speech quality (PESQ), speech intelligibility (ESTOI), voice activity detection, and speech coding (codec presence and bitrate). We show that neural embedding based combination of spectral features with an LSTM and modulation features with a convolution neural network enable NISA+ to achieve state of the art performance. Particularly, for non-intrusive PESQ and C50 estimation, we show around 15% relative reduction in estimation error compared to our previous best results. We also show that NISA+ can be used to perform targeted data augmentation for generating training data for ASR that matches the signal characteristics extracted from a small sample of data recorded in a target room acoustic environment. We show that a 9.6% word error rate reduction can be achieved relative to an ASR model trained with random augmentation.
Malicious Domain Detection based on Heterogeneous Information Network and Fusion Features

Network attackers use malicious domain to perform illegal activities such as online fishing and cyber fraud. This seriously endangers Internet users’ security and challenges national network management. Since domain resolution is an important step to obtain network resources, rapid and effective identification of malicious domain is an important technical means of network security. The existing malicious domain detection methods have two deficiencies: one is that the feature discrimination is not strong, resulting in the low accuracy in practical application; the other is that the training and recognition speed of detection model is slow. A new method based on heterogeneous information network and fusion features, named MDND-HMF model, is proposed in this paper. The model can comprehensively extract multiple features such as domain relationship, text and statistical features from DNS resolution traffic. The comprehensive experiments on the real-world dataset compared with two representative methods indicate that our method has the highest recognition accuracy of malicious domain, up to 86.7%. And the training time of MDND-HMF model is the shortest, which is nearly an order of magnitude lower than the method with the second highest accuracy. Our work provides a practical and effective solution for malicious domain detection.
Robust Estimation of Gaussian Mixture Models Using Anomaly Scores and Bayesian Information Criterion for Missing Value Imputation

The Expectation-Maximization algorithm is a very popular approach for estimating the parameters of Gaussian mixture models (GMMs). A known issue with GMM estimation is its sensitivity to outliers, which can lead to poor estimation performance depending on the dataset under consideration. A common approach to deal with this issue is robust estimation, which typically consists of reducing the influence of the outliers on the estimators by weighting the impact of some samples of the dataset considered as outliers. In an unsupervised context, it is difficult to know which sample from the database corresponds to a normal observation. To that extent, we propose to use within the EM algorithm an outlier detection step that attributes an anomaly score to each sample of the database in an unsupervised way. A modified Bayesian Information Criterion is also introduced to efficiently select the appropriate amount of outliers contained in a dataset. The proposed method is tested on a benchmark remote sensing dataset coming from the UCI Machine Learning Repository. The experimental results show the interest of the proposed robustification when compared to other benchmark imputation procedures.
All-to-All Personalized Communication on Fat-Trees Using Latin Squares

Interconnection networks have a key role in systems with a high number of processors, and are chosen according to their capability of realizing different communication schemes. The all-to-all personalized communication is a relevant commu-nication pattern for many multiprocessor applications, and has been extensively studied in the past for many network topologies. In particular, when using multistage interconnection networks, the method based on the realization of a sequence of permutations forming a Latin square is very advantageous. In particular, a method for realizing the Latin square consisting of the identity permutation and its $N-1$ rotations, $\mathcal{R}\mathcal{L}\mathcal{S}$, has been proposed for the Butterfly-Butterfly network. In this paper, we consider the Fat-Tree, a popular interconnection network used in many distributed systems as well as in Data Centers, and show how to modify and extend the method for the $\mathcal{R}\mathcal{L}\mathcal{S}$ to work on them, whatever the size of the network and switches considered. We then consider also the Slimmed Fat-Tree, that is a cheaper network in terms of circuitry, and for this reason, it can be conveniently used in very large systems. The proposed method can realize the $N$ permutations of a Latin Square in pipeline fashion in optimal time, that is $O(N)$.
An Overview of Latest Methods in Deep Learning for Automated Identification of COVID-19 Infection from Chest X-rays and CT-Scan Images

COVID-19 is a widely spread pandemic that crippled almost all countries and created a critical impact on the quality of human life. The outbreak was started in 2019, and during past years, the coronavirus has undergone various mutations and generated dangerous variants. The best path to control the spread of the coronavirus is with rapid testing and isolation. Among various tests, X-rays and CT-scan images captured from the chest and lungs attracted clinicians and researchers with high sensitivity. Many researchers have developed different automated methods for the identification of COVID-19. Among various automated methods, deep learning-based techniques obtained favourable results in the automation of COVID-19 detection. This paper proposes a detailed review of the latest deep learning methods that are utilized for COVID-19 identification from X-rays and CT scans. The recent publications are carefully choosed from the literature, and a detailed review is conducted. The literature search was conducted in various databases such as Google Scholar, PubMed, and Scopus. The proposed review will be helpful for future researchers in the area of COVID-19 research.
Gait tracking control of biped robot based on adaptive gait switching algorithm

In order to make the walking gait of biped robot more human like, this paper takes the human walking data as the expected gait of robot, and uses the periodic characteristics of gait, proposes a gait tracking control strategy of Biped Robot Based on adaptive gait switching algorithm. Firstly, this paper establishes the complete dynamic models of left leg support phase (LSP) and right leg support phase (RSP) based on Lagrange method, then designs the corresponding LQR gait tracking control strategy, and uses the adaptive weighted particle swarm algorithm (A WPSO) to obtain the optimal controller parameters. Finally, the threshold range of plantar contact force in two periods are estimated based on the adaptive mechanism, and the occurrence of gait switching is detected according to the defined decision rules, thus trigger the control strategy in the next stage to realize the walking tracking control of biped robot. The experimental results show that only two LQR controllers to realize the accurate tracking of the desired gait of the biped robot, and the maximum gait speed reaches two steps/s, which is close to the human gait speed. Compared with other methods, the gait is more human like.
Collaborative AUV Localization and Tracking of an Underway Ship with Adaptive Pinging and a Planner for Trilateration

The efficacy of 3 collaborative autonomous under-water vehicles (AUV) to track a dark ship is studied in a scenario where the ship has entered a marine protected area through a choke point. This is performed in simulations. The objective is to quantity the advantage of mobile transducers over traditional moored long baseline (LBL) transponders. An underwater environment with a constant sound velocity profile captures the one-way travel time inter-AUV communications as well as the two-way travel time for AUVs ranging the ship. The novel contributions include an assessment of the ship localization error given the self-localization error of the three AUVs and their range measurement uncertainty. Another contribution is an adaptive ping strategy, which potentially gives more accurate ship localization by scheduling the pings to arrive simultaneously at the ship. The third contribution is a collaborative AUV mission planner which strives to increase the range that the 3 AUVs can trilaterate the ship over. The results show that the adaptive ping strategy and the collaborative AUV mission planner are effective in reducing ship localization error. With these encouraging results, future work includes a refined environment model that integrates BELLHOP and considers environmental and range dependent communications.
High-Level Fault Diagnosis in RISC Processors with Implementation-Independent Functional Test

We propose a novel functional approach for Software-Based Self-Test generation and fault diagnosis for RISC processors in the case when low-level implementation details are not available. To cope with the complexity of the problem, we partition the processor into high-level functional modules, defined by the instruction set and architectural description given in manuals. We propose a novel equivalent model for modules under test (MUT), which allows separate test and diagnosis of control and data faults. The model allows coping with multiple faults, which may mutually mask each other. A novel high-level control fault model was developed, which covers a broad class of high-level functional faults, comparable to that used in memory testing, and also a large class of structural faults such as stuck-at-faults (SAF), conditional SAF, multiple SAF and bridging faults. Implementation independent fault diagnosis cannot provide fault location with an accuracy of logic gates. However, the proposed method affords to distinguish functional faults in the control and data parts of MUT and provides diagnostic resolution of functional faults with the granularity of instructions and bit-slices thanks to the test structuration and the new fault model.
A Parkinson's Bradykinesia Recognition System Based on Deep Learning Method

Parkinson's disease is a neurodegenerative disease common in middle-aged and elderly people, and bradykinesia is one of the most obvious symptoms. In this study, A deep learning-based Parkinson's bradykinesia recognition system is designed which has an inertial sensor-based data acquisition system. It can wirelessly collect bradykinesia kinematic information by wearing it on the wrist and ankle of Parkinson's patients. In addition, a method for identifying the symptom of Parkinson's bradykinesia based on Long Short Term Memory (LSTM) Network is proposed, with an accuracy of 98.6%. It provides a feasible method to identify Parkinson bradykinesia to assist the diagnosis and treatment.
Refined Feature Vectors for Human Emotion Classifier by combining multiple learning strategies with Recurrent Neural Networks

The speech emotion recognition (SER) system categorizes human emotions based on contextual features. However, it is seriously affected during the signal transmission in which the quality of real­time speech processing is degraded in the SER system. This paper presents refined feature vectors for human emotion classifiers based on multiple learning strategies combined with recurrent neural networks (Refine­HE­RNN). It extracts spatial emotional vectors by observing speech signals for contextual feature dependency through the multiple learning (ML) approach. It computes signal interpretation, emotional cues, and input correction by using the skip connection (SC) module in the residual block of the ML strategy. The fused layer is simple to concentrate derived features that support automatic learning of classifying different human emotions. For experimental purposes, standard IEMOCAP and MSP­IMPROV datasets are considered for proposed method validation. Results convey that the proposed method has significant improvement (in terms of percentage closer to 80% higher than the existing CNN result) in the feature recognition and is flexible for real­time implementation in the SER system. Moreover, it can extend to automatic sensing of human emotion with the help of a light weighted RNN framework.
Description of Multipole in f-Electron Systems

A systematic description of multipole degrees of freedom is discussed on the basis of the Stevens' operator-equivalent technique. The generalized Stevens' multiplicative factors are derived for all of the electric and the magnetic multipoles relevant to f -electron systems. With extensive use of the Stevens' factors, we express the spatial dependences of the electric and the magnetic fields, and the electric and the magnetic charge densities of localized f electrons. The latter is utilized to draw wave functions including their magnetic profile in addition to their shape with the charge density. The definite relation between the operators as quantum-mechanical variables in a multipole exchange model and the multipole moments in expansion of electromagnetic fields is given. The general treatments for the exchange model with the random-phase-approximation (RPA) susceptibility and the Ginzburg–Landau free-energy expansion are discussed, using Ce x La 1- x B 6 as a typical example. The representative formula .
Risk-Averse Decentralized Optimal Scheduling of a Virtual Energy Hub Plant Equipped with Multi Energy Conversion Facilities in Energy Markets

Distributed multi-energy systems, in addition to their advantages, pose significant challenges to future energy networks. One of these challenges is how these systems participate in energy markets. To overcome this issue, this paper introduces a virtual energy hub plant (VEHP) comprised of multiple energy hubs (EHs) to participate in the energy market in a cost-effective manner. Each EH is equipped with multiple distributed energy resources (DERs) in order to supply electrical, heating and cooling loads. Moreover, an integrated demand response (IDR) program and vehicle-to-grid (V2G) capable electric vehicles (EVs) are taken into consideration to enhance the flexibility to EHs. The manager of the VEHP participates in the existing day-ahead markets on behalf of EHs after collecting their bids. Since EHs are independent entities, a hybrid model of mobile edge computing system and analytical target cascading theory (MECATC) is proposed to preserve data privacy of EHs. Further, to tackle the uncertainty of renewables, a robust optimization method is applied. Obtained results corroborated the proposed scheduling is efficient and could increase the VEHP’s profit about 21.4% in light of using flexible technologies.
Joint Task Offloading and VM Placement for Edge Computing with Time-Sequential IIoT Applications

In this paper, a multi-layer edge computing frame-work for the virtual machine (VM) placement and computation offloading in industrial Internet of Things (IIoT) is proposed. Unlike most existing works, we focus on addressing the temporal dependency among tasks in an IIoT task flow, and consider that there is a stringent requirement on its completion time (including the transmission time, computation time and waiting time). For striking a balance between the system completion time and the energy consumption while satisfying the storage capacity of edge servers (ESs), completion deadline of time-sequential task flows, and placement requirements of VMs, we design a many-to-one matching game (MGVDA) to jointly determine the optimal VM placement and task offloading decisions. Finally, we prove that the resulted matching game solution is effective and stable. Simulation results examine the efficiency of the proposed MGVDA and show its superiority over the counterparts.
Learning to Optimize Satellite Flexible Payloads

This paper proposes an optimization technique for satellite systems with flexible payloads. Unlike current satellites whose per-beam capacity is fixed, forthcoming payloads will have bandwidth and power allocation reconfiguration capabilities allowing the operators to modify the offered capacity. Assuming a generic flexible payload architecture, this paper introduces an op-timization technique that is able to provide an efficient bandwidth and power allocation that fulfil the user terminals rate requests. Furthermore, we introduce a deep learning regression algorithm able to reproduce the mapping of the proposed optimization technique with a very reduced computational complexity. By using the output of the optimization technique as ground truth, we design a deep neural network that behaves very similar to the optimization problem yet with a dramatically reduced computational time. Numerical results show the benefits of the proposed technique and in particular, we observe two order of magnitude computational time decrease when using the deep learning approach compared to the classical optimization technique yet preserving almost the same performance.
Multi-band Masking for Waveform-based Singing Voice Separation

Singing Voice Separation (SVS) is the task of isolating the vocal component from a given musical mixture. In recent years there has been an increase in both the quantity and quality of SVS techniques that operate in the waveform domain and have an encoder-separator-decoder structure, where the separator processes a latent representation of the waveform produced by the encoder. In this work, we propose a parallel multi-band modification for this family of architectures, that splits the latent representation provided by the encoder in multiple sub-bands and then processes each band in isolation, using multiple separators, so as to better exploit the internal correlations of each sub-band. We investigate the effect of our proposed modification on Conv-TasNet, a widely used architecture adhering to the encoder-separator-decoder paradigm. The results indicate that the proposed modification improves the overall performance without altering the network size, and offer insights on its scaling capabilities as well as its applicability in other architectures that follow this general paradigm.
Line Spacing Tunability in Multiwavelength Random Fiber Laser based on Series of Lyot Filters

We have demonstrated a tunable multiwavelength random fiber laser based on a series of Lyot filter. From the HWP adjustment, the obtained line spacing is 0.4 nm, 1 nm or 2 nm which selectable according to the effective length based on the length of polarization maintaining fiber of PMF1+PMF2, 2PMF1 and PMF1, respectively. The peak power of lasers increases with power of erbium-doped fiber amplifier until the highest peak power of -5 dBm, while the line spacing is fixed at 2 nm.
Analysis and Implementation of different non-isolated Partial-Power Processing Architectures based on the Cuk Converter

This paper presents the analysis and study of different partial-power processing architectures based on the Cuk converter. Thus, the non-isolated topologies analyzed have been considered operative as voltage source converter (VSC). Likewise, the study of the converters is based on the continuous conduction mode (CCM). The partial-power topology has some advantages, such as high power density, small size, the decreased voltage in semiconductor devices, etc., this is because the DC-DC converter only processes a fraction of the total power. The purpose has been to compare the different topologies, Full-Power, Partial-Power, and hybrid-converters, in order to observe the advantages and drawbacks in each case. Among the parameters to be studied is the voltage stress in semiconductors as well as the voltage gain in each architecture. The effectiveness of these new converter architectures has been validated by the Matlab/Simulink software simulation. In this way, different simulation results are presented and analyzed throughout the paper. The purpose of this paper has been to study and explore the usefulness of the non-isolated Cuk converter with partial-power processing architecture for industrial power applications.
Carbon Dots/Silver Nanotriangular Based Localized SPR Biosensors for Enhancement of Creatinine Detection

This work reports the application of carbon dots (CDs) cooperated with silver nanotriangular (AgNT) for the enhancement of creatinine detection of localized surface plasmon resonance biosensors. Creatinine is detected through the hydrogen bonding between the functional group of the sensing material creatinine. Carbon dots were successfully synthesized via a hydrothermal process using citric acid and ethylenediamine as precursors. AgNT was successfully synthesized via the direct chemical reduction method. The appearance of four peaks of carbon dots in UV-visible analysis, at 206, 228, 242, and 384 nm. In PL spectroscopy, the peak of carbon dots, silver nanotriangular (AgNT), and carbon dot/silver nanotriangular (CDs/AgNT) composite that dissolves in an aqueous solution appeared at 451, 447, and 446 nm each. The carbon dots cooperate with silver nan-otriangular before the sensor performance analysis. In localized surface plasmon resonance, the carbon dot/silver nanotriangular exhibits the sensitivity of 4.07 nm(mg/dL)-1 respectively.
Creating Consistent Privacy Notices by Translating Code Segments into Privacy Captions

A privacy notice is a short descriptive statement that describes to users how their information is used and why? When a mobile application uses any personal information of the user, such as location, it must provide them with a privacy notice. However, creating concise and accurate privacy notices with each update to an application is a challenging task. Previous efforts have focused on creating these notices through questionnaires or predefined templates which do not make application source code traceable with privacy notices. Lack of traceability between source code and privacy notices leads to inconsistencies and inaccuracies. In this paper, we discuss our approach to creating privacy captions, short sentences that describe how and why users’ personal information is used. by translating the application’s source code. In this work, we explain our plan to implement our approach and demonstrate the steps we have implemented so far.
M-estimate based Proportionate Normalized Adaptive Subband Filter with Combined Step-size

The M-estimate based proportionate normalized subband adaptive filter (M-PNSAF) exists a performance compromise caused by the constant step-size. To effectively deal with this problem, we propose to take advantage of large and small step-sizes through the adaptation of the mixing factor to improve the M-PNSAF performance. Different from adaptively adjusting the mixing factor through a sigmoid function, the proposed algorithm minimizes the squared a posterior subband errors at each iteration to find the optimal time-varying mixing factor. Finally, simulation results in impulsive noises corroborate the effectiveness of the proposed algorithm.