Miracle-dz/UniFAR-FTUs huggingface dataset:
Improve Mandarin Speech Recognition Using Simple Recurrent Units and SpecAugment

Simple Recurrent Units (SRU) is one kind of Recurrent Neural Networks, which has been explored in some tasks. However, SRU hasn’t been widely used in automatic speech recognition task. In this paper, we explore one SRU to improve the hybrid Connectionist Temporal Classification and Attention based Mandarin speech recognition system using bidirectional long short-term memory (Bi-LSTM). Besides, we also use the data augmentation strategy of SpecAugment to further improve the system. The experiments on Aishell-1 dataset show that bi-directional (Bi-SRU) outperforms BiLSTM as encoder, and some optimization strategies like label smooth and scheduled sampling also improve the accuracy. The model trained using Bi-SRU and SpecAugment achieves the best performance in the experiments.
Label Generation Network based on Self-selected Historical Information for Multiple Disease Classification on Chest Radiography

Deep learning has made significant break through’s in image classification, but accurate diagnosis on chest radiography remains challenging due to a variety of potential diseases contained in one scan. Complex relations among diseases have significant clinical meanings, but are always ignored in most of previous work. Thus in this paper, we propose a novel Label Generation Network (LGN) which treats the label sequence as the caption of a radiology image and utilizes RNN to generate the disease labels according to the semantic relations and co-occurrence dependency among them. However, the sequential generation process of RNN makes it hard to capture the complex topological relations among diseases. To mitigate this problem, a Historical Information Module (HIM) is especially introduced to LGN, in which all the generated labels are fully considered when generating a new label. Moreover, a specific self-attention mechanism is applied in HIM to learn the topological disease relations and utilize them to select useful historical information which can provide positive guidance to the prediction of new label. Very positive results have been obtained in our experiments on the benchmark dataset of Chest X-ray14, which significantly outperform the state-of-the-art methods.
Application of Machine Learning to Lightning Strike Probability Estimation

The risk of a lightning strike to a structure is influenced by geometry, the type of material of the structure, and proximity to other structures. The probability of lightning strike to different points on an object can be determined by applying the concept of probability modulated collection volume using the dynamic electro-geometrical model (DEGM). The numerical computation of the DEGM is computer resource-intensive and requires extensive programming and analysis to implement, which may be difficult to apply by engineers setting up lightning protection systems in the field. This study explores the feasibility of applying an artificial neural network (ANN) using a network with 18 hidden neurons and the Levenberg-Marquardt algorithm for training the model. The strike probability predictions by the neural network give close values to the numerical results. The ANN approach is further supported by predictive equation-based models developed using data pattern recognition and curve fitting techniques, as easy to use alternatives to the numerical DEGM simulations. The ANN model had an overall regression R2 of 1, and it performed better than the equation-based model.
Observer-Based Excitation Control for Transient Stabilization of the Single Machine Infinite Bus System

The availability of excitation controllers to enhance transient stability has regained significant relevance in recent years, due to the unprecedented ongoing changes in power systems. Yet, the practical deployment of many reported control schemes is hampered by the fact that their implementation requires the measurement of the full state vector. Our main contribution is to address this fundamental obstacle by proposing an observer-based excitation controller using modern phasor measurement technology. For this purpose, a linear time-varying observer scheme for the generator frequency and the internal voltage is derived. This observer is then combined with a classical passivity-based excitation controller. Stability of the resulting nonlinear observer-based closed-loop system is shown by deriving an ISS-based separation principle. The performance of the proposed approach is demonstrated via simulation example.
Survey of machine learning application in transactional memory

Transactional memory (TM) is a paradigm that removes the need for using locks and makes it easier for programmers because it reduces work when it comes to synchronization. TM enables the atomicity and isolation of concurrent threads. TM can be realized in software (STM) or with hardware support (HTM) or hybrid. In this survey different machine learning (ML) techniques are explored to improve performance, energy effectiveness, simplicity, and benefits of using TM. Various methods as thread mapping, self-adjusting concurrency, etc. are combined with machine learning algorithms. After presenting the techniques, the obtained results are discussed.
Semantic-Aware Graph Convolutional Networks for Clinical Auxiliary Diagnosis and Treatment of Traditional Chinese Medicine

Traditional Chinese Medicine (TCM) clinical informatization focuses on serving user-oriented health knowledge and facilitating online diagnosis. Regularities are hidden in clinical knowledge play a significant role in the improvement of the TCM informatization service. However, many regularities can hardly be discovered because of specific data-challenges in TCM prescriptions at present. Therefore, in this article, we propose an end-to-end model, called Semantic-aware Graph Convolutional Networks (SaGCN) model, to learn the latent regularities in three steps: (1) We first construct a heterogeneous graph based on prescriptions; (2) We stack Semantic-aware graph convolution to learn effective low-dimensional representations of nodes by meta-graphs and self-attention; (3) With the learned representations, we can detect regularities accurately by clustering and linked prediction. To the best of our knowledge, this is the first study to use metagraph and graph convolutional networks for modeling TCM clinical data and diagnosis prediction. Experimental results on three real datasets demonstrate SaGCN outperforms the state-of-the-art models for clinical auxiliary diagnosis and treatment.
Attitude Control and Reachability Analysis for Unmanned Helicopters via the Zonotopic Kalman Filtering

This paper presents a new methodology to develop an integrated system that is used to stabilize attitude dynamics and to estimate flight envelopes of an unmanned helicopter. This system consists of two blocks: an observer-based feedback controller and a reachability-based flight envelope estimator. Information about the helicopter’s state is only partially available through measurement. Thus, an element of this system playing an important role to provide information about unmeasurable state variables is an observer, which is then referred to as a zonotopic Kalman filter. Such a Kalman filter is constructed to yield sets of state estimates of the helicopter in terms of zonotopes. Each state-zonotope estimate is defined by a center vector and a generator matrix, which respectively represent a nominal state estimate and possible variations around the center vector. The state-zonotope estimates are utilized by the integrated system to produce control input signals and flight envelope estimates of the helicopter via a zonotopic reachability analysis. The latter is useful for enhancing operator’s awareness about helicopter dynamics, especially when the helicopter suffers from actuator faults. It is well known that the actuator faults may give rise to control effectiveness degradation and accidents in the worst cases. The benefit of applying the zonotopic Kalman filter is shown via an example where noise perturbations upon the helicopter do not possess Gaussian properties. In this case, the zonotopic Kalman filter outperforms an ordinary stochastic Kalman filter in providing meaningful estimates of the helicopter’s state variables.
Implementation of Maximum Power Point Tracking on PV System using Artificial Bee Colony Algorithm

Implementation of Solar thermal energy as a source of renewable electricity is currently being developed. The main problem with photovoltaic systems is the result of power efficiency is low. The maximum power point tracking (MPPT) method can increase the efficiency of photovoltaic output power. This research will use the MPPT method with an artificial bee colony (ABC) algorithm. MPPT design will be simulated using Power Simulation (PSIM) software. Simulation results will be compared with no MPPT and MPPT human psychology optimization (HPO) algorithm. The results show MPPT ABC gets the best average accuracy from the average accuracy without MPPT and MPPT HPO, which is 99.95%. And the MPPT ABC has a response time of MPP tracking faster than MPPT HPO, during irradiation 800 W/m2, 900 W/m2, 1000 W/m2.
Expressed and Private Opinion Dynamics on Influence Networks with Asynchronous Updating

In this paper, an asynchronous discrete-time opinion dynamics model on a social influence network is considered. At each time instant, a single individual activates and updates two state variables simultaneously. The individual’s new private opinion is a weighted average of her current private opinion, the expressed opinions of her neighbors, and a constant prejudice. Meanwhile, the individual’s new expressed opinion is equal to her current private opinion, altered due to a pressure to conform to the public opinion as perceived by the individual, being the average expressed opinion among her neighbors. We analyze the system for social networks which are rooted, and show that if no individual holds a prejudice, then a mild assumption on the activation sequence of the individuals guarantees convergence. In particular, the expressed and private opinions of all individuals converge to the same value exponentially fast, with two lower bounds on convergence speeds based on two different assumptions on the network topology. Simulations are provided to illustrate the result, and provide support to the conjecture that the system dynamics may converge even if individuals hold an existing prejudice.
Industrial Wastewater Treatment Configuration: Insights from a Northern Italy Textile Manufacturing District

Industrial wastewater treatment is getting increased attention from academics, practitioners and regulators, due to the environmental hazard of discharging poorly treated wastewater into the environment. This paper analyzes the case of Como’s textile district in Italy, to explore what factors are considered by firms in selecting the most appropriate wastewater treatment system configuration. The case studies highlighted that Como’s wastewater consortium benefits the firms in the district, and it is a better solution compared to the presence of suboptimal private treatment plants. The firms mentioned internal stakeholders, factors related to the wastewater and technology, and economics as the most relevant.
Dual-Loop and Parity-Time-Symmetric Optoelectronic Oscillator with Strong Sidemode Suppression

A dual-loop and parity-time (PT) symmetric optoelectronic oscillator (OEO) is proposed to generate microwave signal with high sidemode suppression ratio. The parity symmetry is implemented with two optical carriers of different optical power, which are used to realize two coupled resonators in the parameter space of optical wavelength. The dual-loop architecture is implemented by adopting optical fiber of different physical lengths. The joint mode-selection mechanism of the dual-loop configuration and the PT symmetry will result in a strong sidemode suppression ratio, as compare to that when a single technique is used. In the experiment, a microwave signal with a frequency of 10 GHz and a phase noise of −112.4 dBc/Hz at an offset frequency of 10 kHz is generated. A high sidemode suppression ratio of 60.71 dB is achieved.
Microwaves in Chemistry

The unique properties of microwaves make them useful in many diverse applications across a wide range of fields, spanning much of engineering and science. In the chemical sciences, microwaves provide a toolkit of electric and magnetic effects with which, at high power, unconventional heating modes can be used to produce new materials not obtainable by conventional heating. At low power, unique microwave properties can be used to make revealing spectroscopic measurements. In this review, we consider the current outlook for microwaves in chemistry beginning with the theoretical framework for our understanding of microwaves interactions and the causes of results observed. We then survey major application areas including in synthesis and emerging areas in catalysis, energy, and environmental applications. Finally, we review new concepts in dielectric and magnetic spectroscopy at microwave frequencies with a focus upon dielectric property measurement and electron paramagnetic resonance. This nonexhaustive review seeks to highlight important and emerging areas in the chemical sciences and put into context recent developments and advances in our understanding of microwave applications in this diverse area of science and engineering.
Bypassing Anti-Analysis of Commercial Protector Methods Using DBI Tools

As most malware is infectious, anti-analysis and packing techniques supported by commercial protectors are conventionally applied to hinder analysis. When analyzing to detect and block such protected malware, it is necessary to do so in a virtual environment to prevent infection. In terms of packing, it is necessary to analyze using dynamic binary instrumentation (DBI), a dynamic analysis tool, which is advantageous for unpacking because DBI inserts code at run time and analyzes it dynamically. However, malware terminates on its own when it detects a virtual environment or DBI due to anti-analysis techniques. Therefore, it is necessary to also bypass anti-VM and anti-DBI techniques in order to successfully analyze malware in a virtual environment using DBI. It is very difficult for analysts to bypass anti-VM and anti-DBI techniques that are used in commercial protectors because analysts generally have little information on what methods are used or how to even bypass these techniques. In this paper, we suggest guidelines to aid in easy analysis of malware protected by anti-VM and anti-DBI techniques supported by commercial protectors. We analyzed the techniques used by five of the most common commercial protectors, and herein present how to bypass anti-VM and anti-DBI techniques supported by commercial protectors via a detailed algorithm analysis. We performed a bypass experiment after applying each commercial protector to 1573 executable files containing vulnerabilities provided by the National Institute of Standards and Technology (NIST). To our knowledge, this is the first empirical study to suggest detailed bypassing algorithms for anti-VM and anti-DBI techniques used in commercial protectors.
Optimal Design of Flux Coupling Differential Zero-Sequence Current Transformer

With the operation of neutral point grounding syste m with small resistance, zero sequence current transformer ha s the problems of huge volume and insufficient accuracy in pra ctical engineering. Using flux constrained zero sequence curren t transformer can solve the problem of crossing three-phase ca ble and reduce the volume of equipment. At present, the design of flux constrained zero sequence current transformer needs t o be studied. Therefore, an optimal design method of flux const rained zero sequence current transformer is proposed. Firstly, the working principle of flux constrained zero sequence curren t transformer is introduced. Secondly, the optimization directio n of flux constrained zero sequence current transformer is pro posed, and the winding method and air gap optimization desig n of flux constrained zero sequence current transformer are ca rried out aiming at accuracy. Finally, the finite element model and circuit simulation model are established for simulation cal culation, and a flux constrained zero sequence current transfor mer is developed and tested. Simulation and experimental results show that the proposed method can be used to design high-p recision flux constrained zero sequence current transformer.
Power Module on Copper Lead Frame with Novel Sintering Paste and SnSb solder

Die attach and interconnect materials for power module device studied in this experiment included pressure-less sintering paste and SnSb solder. The initial evaluation on various pressure-less silver (Ag) sintering die attach pastes showed that type A paste doesn't form the void after cure, type B with void seen and type C paste has vertical vein likes void seen after oven cure. During copper clip attachment, undesired interconnect seen which caused short circuit between gate and source pads. The proper control during clip attachment was carried out, a good interconnect joint formation without short circuit between gate and source pads was achieved for both pressure-less Ag sintering paste and SnSb solder. Flux cleaning process assisted in removing flux residual on the SnSb reflowed samples.
IoT Device Auto-Tagging Using Transformers

The IoT platform can identify a device matching the data only when securing the information on a large amount of IoT devices in advance. However, in a situation where a lot of companies release a variety type of IoT, it is not easy to retain information about all IoT devices. That is, it is a difficult situation where the current IoT platform has difficulty in analyzing unstandardized tagging information. Therefore, this paper provides a technique of ‘Tagging’ that automatically identifies the device based on information collected from IoT devices or sensor devices. This technique was developed based on the natural language processing algorithm based on the attention mechanism among the machine learning models.
Modeling of Grid Connected PMSG based WECS

Nowadays, the use of wind energy for generating electricity is increasing and with the advancement of power electronic converters use of permanent magnet synchronous generator (PMSG) is become popular as it has several advantages over other variable speed wind turbine generators. The main contribution of this research work is to develop a mathematical model of grid-connected PMSG based wind energy conversion system (WECS). The model is simulated using MATLAB SIMULINK (Simulink root library only). In this research work modeling of different components of WECS which includes wind turbine model, PMSG model, a functional model of ac to dc converter and voltage source inverter (VSI) is done. Converters are mathematically model using the switching functions.
How much can Fog Computing enhance performances of heterogeneous delay-sensitive services in Smart Cities?

The growing number of urban residents leads to an intensive sharing of the natural and industrial resources of a city, as well as the embeddedness of billions of equipments and systems in the urban environment. Thus, the integration of Information and Communication Technology (ICT) and the Internet of Things (IoT) paradigm results on a “smart” city where “smart” services and applications, like smart surveillance, smart healthcare and smart transportation, are performed. Moreover, the Cloud computing paradigm is expected to provide on-demand software and hardware resources for gathered data treatment in such environment. However, the IoT/Cloud integration may induce higher energy consumption level and increased response-time, which is not suitable especially for delay-sensitive applications. This is because of the data processing by distant cloud datacenters, located in the core of the network, far from end-users. Thus, Fog computing seems to be a suitable solution to enhance delay-sensitive applications performance in an IoT/Cloud integration for smart cities, as, with fog nodes, data is processed at the edge of the network, closer to end-users. Several previous works showed the benefit of fog computing over IoT/Cloud integration, simulating one specific application. However, in this paper, we propose a fog-based model for delay-sensitive services in a smart city, simulating four realtime applications simultaneously. Our model records better results in terms of latency, network usage and energy consumption, compared to a Cloud-only reference model. Simulation is carried with iFogSim tool to perform four different real-time services, representing healthcare, transportation, content processing and public safety fields in a smart city.
Analysis of Two-Step Random Access Procedure for Cellular Ultra-Reliable Low Latency Communications

Due to the emergence of Internet of Things (IoTs), it can be expected that the bandwidth provided by cellular systems might be consumed up soon. Some applications of them are delay-sensitive such that it would be critical to guarantee random access (RA) delay less than a threshold. Since the existing Long-Term Evolution-Advanced (LTE-A) RA procedure is a four-step signaling procedure, it may not be suitable for such delay-sensitive applications due to its time-consuming procedure. This work investigates a two-step RA procedure for 5G New Radio systems, where RA preamble and bandwidth request message are transmitted at the same time. First we show that the operating region of two-step RA procedure can be divided into three regions such as unsaturated stable, bistable, and saturated regions in terms of a packet generation probability, retransmission probability, the number of devices, and the number of RA preambles. To see whether RA delay requirement of delay-sensitive applications can be guaranteed, this work shows that the system should run under unsaturated region and derives RA delay distribution when IoT devices employ geometric probability backoff (GPB) or uniform window backoff (UWB) algorithm. We then examine the probability that the RA delay would be larger than some threshold depending on the operation regions.
Spatio-Temporal Deep Learning Framework for Traffic Speed Forecasting in IoT

The Internet of Things (IoT) enables us to collect traffic data from multiple sources, which makes it possible to predict traffic speed in time with big traffic data. This article presents a novel spatio-temporal deep learning framework that aims to provide accurate and timely traffic speed forecasting. To extract the spatial and temporal features of traffic data simultaneously, this framework combines two different deep learning models, namely convolutional long short-term memory (ConvLSTM) and the graph convolutional network (GCN). Specifically, the ConvLSTM model is used to learn the temporal dynamics of traffic data to extract temporal features. On the other hand, the GCN model is used to learn the spatial complexity of traffic data to extract spatial features. Experiments show that the prediction performance of our framework at different time horizons outperforms three baseline approaches.