Miracle-dz/UniFAR-FTUs huggingface dataset:
Speech Emotion Recognition Method Using Depth Wavefield Extrapolation and Improved Wave Physics Model

Speech emotion recognition(SER) task based on acoustic wave always has a large number of features, which brings great difficulties to improve the accuracy of recognition. In this paper, we propose a new speech emotion recognition method, which is based on depth wavefield extrapolation and improved wave physics model (DWE-WPM). The method can improve loss accuracy and feature explosion problem when extracting the features. The schema comes from the wave physics system. After extrapolating the wavefield with a fixed-step depth, we inject the reconstructed waveform into DWE-WPM to simulate the information mining process of Long Short-Term Memory Recurrent Neural Network(LSTM), and then fuse the output features of this model with the sorted HSF features. Finally, the integrated features are injected into BiMLSTM to automatically complete the SER task. Massive experiments were carried out on the emotion corpus of interactive emotional dyadic motion capture (IEMOCAP). The experimental results showed that the weighted average (UA) accuracy of the proposed method can be improved by 21%, which was better than the existing methods of SER from raw wave. The method proposed in the paper proved the effective for SER task.
Determinants of GSCM Systems Performance

Today’s export-based manufacturers face the need to implement green supply chains more efficiently in order to overcome environmental barriers in increasingly competitive export markets and to improve export performance. This study examines the structural relationship between environmental collaboration, green innovation capacity, and performance based on the findings of previous studies in order to identify the factors affecting the green supply chain management (GSCM) performance of Korean export-based manufacturers. The study finds that environmental collaboration in the green supply chain environment is an important driver of green innovation capacity for export-based Korean manufacturers. It also finds that green innovation capacity has a positive effect on export performance through environmental performance. This study establishes a theoretical basis for the systematic study of the structural mechanisms of green supply chains and suggests strategic directions for export-based manufacturing firms’ successful GSCM implementation.
Application of Learning-based Multi-view Stereo in Digital Design

Image-based 3D reconstruction technology has been widely used in automatic driving, medical image reconstruction, positioning and navigation. At present, the digital design is still dominated by drawing 3D models manually, which has some shortcomings such as time-consuming and limited accuracy. To solve the above problems and improve the efficiency of digital design, a learning-based multi-view stereo 3D reconstruction technique is introduced for dense reconstruction, and then a grid model is generated for parametric design. We experimented our method and the results show that it can greatly improve the efficiency of 3D digital design, obtain high precision, and retain the texture information of the object.
An Interpretable Model for ECG Data Based on Bayesian Neural Networks

Heart arrhythmia have been a life-threatening disease to human for a very long time, many techniques and methods have been developed by human experts since the advent of ECG machine, these approaches work well and most of the time patients with heart diseases can be diagnosed accurately, and yet not timely, human experts for heart disease are far from adequate. Deep learning models for heartbeat arrhythmia detection, therefore, have been proposed and investigated during the past few years, they have good results on some data-sets concerning arrhythmia detection and classification, some even outperformed human doctors. However, like most deep learning models, the underlying mechanism has not been justified and explained very well, for example, we cannot interpret the decision momentum of certain models. Concerning that, we developed a general feature extraction framework for ECG data, it can perform various kinds of feature engineering tasks, all of the features have their meaning under a clinical context. We distilled the features on a large ECG data-set, then these features were fed into a Bayesian Neural Network for training. The BNN has a sparsity-inducing prior distribution arranged in a tied manner to learn the importance of every feature concerning the outcome. We evaluated this method for a classification problem and made a comparison between the way our model makes decisions and how field experts do for the same problem, we were able to find out that a) the features extracted from raw ECG data using our framework can be used as an indication for specific heartbeat arrhythmia, b) the mechanism of the model can be interpreted as a decision making procedure by weighing relative metrics just like human experts do, c) the weight of the features extracted from raw ECG data can be used to build a knowledge tree for guidance on diagnosing of certain heartbeat disease.
Context Sentences to Single Vector Compression using Convolutional Transformers for Deep Learning based NLG Tasks

In natural / casual conversations, people tend to have their own unique style of speaking/ chatting; certain phrases only they use, style of typing, customized grammar, nicknames etc. These ‘imperfections’ make the dialogue more human; we are able to identify people from their unique style of chatting. Conversational chatbots trained on clean text won't be able to capture these nuances. We propose a method to capture these quirks in the form of a chatbot with a novel NLG (Natural Language Generation) architecture; In the form of a convolutional transformer. WhatsApp chats between two users were cleaned and exported for training the model. The dataset had Unicode characters for foreign lexicon, emojis, and personalised grammar / texting patterns. The model is able to capture these traits in conversation for short responses.
Research on factors influencing intention to use Smart home devices in Danang

With the outstanding development of the information and communication technology industry, the Internet of Things (Internet of Things) has become an outstanding trend all over the world. Internet of Things can completely change the way people live in the future because of its applications. Smart home is a recent application of the Internet of Things with the aim of improving the quality of life for everyone. Besides the attractive potentials, the adoption of Smart home in general as well as smart home devices in particular has not yet been widely accessed to the mass market. This article proposes a research model to investigate the intention of using Smart home devices of consumers living and working in Danang city based on technology acceptance model. The study is based on 287 samples collected from Danang residents since November 2019, based on quantitative analysis by soft SPSS and AMOS.
Multi-Scale Voxel Class Balanced ASPP for LIDAR Pointcloud Semantic Segmentation

This paper explores efficient techniques to improve PolarNet model performance to address the real-time semantic segmentation of LiDAR point clouds. The core framework consists of an encoder network, Atrous spatial pyramid pooling (ASPP)/Dense Atrous spatial pyramid pooling (DenseASPP) followed by a decoder network. Encoder extracts multi-scale voxel information in a top-down manner while decoder fuses multiple feature maps from various scales in a bottom-up manner. In between encoder and decoder block, an ASPP/DenseASPP block is inserted to enlarge receptive fields in a very dense manner. In contrast to PolarNet model, we use weighted cross entropy in conjunction with Lovasz-softmax loss to improve segmentation accuracy. Also this paper accelerates training mechanism of PolarNet model by incorporating learning-rate schedulers in conjunction with Adam optimizer for faster convergence with fewer epochs without degrading accuracy. Extensive experiments conducted on challenging SemanticKITTI dataset shows that our high-resolution-grid model obtains competitive state-of-art result of 60.6 mIOU @21fps whereas our low-resolution-grid model obtains 54.01 mIOU @35fps thereby balancing accuracy/speed trade-off.
Using Semantic Information to Improve Generalization of Reinforcement Learning Policies for Autonomous Driving

The problem of generalization of reinforcement learning policies to new environments is seldom addressed but essential in practical applications. We focus on this problem in an autonomous driving context using the CARLA simulator and first show that semantic information is the key to a good generalization for this task. We then explore and compare different ways to exploit semantic information at training time in order to improve generalization in an unseen environment without fine-tuning, showing that using semantic segmentation as an auxiliary task is the most efficient approach.
The Design of Industrial Security Tasks and Capabilities Required in Industrial Site

As companies’ competitiveness has shifted to technology and other intellectual assets and the method of intrusion and extortion on them, the demand for industrial security professionals who can respond professionally them have also become more advanced. The key is to systematically operate the education system and qualification system based on the demands of industrial sites, and the national standardized system created by the government for this purpose is the National Competency Standards. This study was conducted to analyze the current status of industrial security-related tasks and job capabilities within the NCS and indirectly identify the demand of industrial sites for Industrial security. This study derived security-related tasks, tasks requiring security capabilities, and required security capabilities through keyword searches, and as a result, was able to identify Indus-trial security demands that were different from existing security, such as Information security. It is expected that the results of this study, combined with further demand excavation and survey of industrial sites, will be utilized as the basic data for NCS development of industrial security
Hard Example Mining based Adversarial Autoencoder Recommendation Algorithm

Commonly used datasets in recommendation research suffer from unbalanced data distribution, sparsity, and different user rating preferences. All these problems affect the quality of recommendation. Thus, this paper proposed a recommendation model by combining hard example mining with adversarial autoencoder. Considering the difference in users’ preference, Mean Model based triplet loss algorithm was introduced to classify the dataset into positive and negative samples and thus improve the quality of the training data. Using classified samples, the rating prediction model was trained from both reconstruction and adversarial aspects. Adam optimization algorithm was used to calculate different update gradients for different parameters. Experimental results show that the recommendation model improves the recommendation accuracy significantly, and several performance indicators are better than baseline models.
Local Traffic-Based Energy-Efficient Hybrid Switching for On-Chip Networks

Advanced flow control mechanisms employed by modern on-chip networks are the reasons of large energy footprint and long per-hop latency. On the other hand, dated and simpler flow controls such as circuit switching can draw far less power and offer an end-to-end latency analogous to wire delay. In this paper, we present a hybrid flow control mechanism, which mixes both virtual channels and circuit-switching, to provide a latency-competitive and energy-efficient on-chip network design. Contrary to existing hybrid switching designs, our proposal is based on local traffic so that circuits are formed without the knowledge of end-to-end traffic. When compared to on-chip networks with virtual channels, our proposal achieves a very competitive latency per flit, for up to 4% lower, while also dramatically suppressing the energy per flit by up to 18%.
EGFR Assessment in Lung Cancer CT Images: Analysis of Local and Holistic Regions of Interest Using Deep Unsupervised Transfer Learning

Statistics have demonstrated that one of the main factors responsible for the high mortality rate related to lung cancer is the late diagnosis. Precision medicine practices have shown advances in the individualized treatment according to the genetic profile of each patient, providing better control on cancer response. Medical imaging offers valuable information with an extensive perspective of the cancer, opening opportunities to explore the imaging manifestations associated with the tumor genotype in a non-invasive way. This work aims to study the relevance of physiological features captured from Computed Tomography images, using three different 2D regions of interest to assess the Epidermal growth factor receptor (EGFR) mutation status: nodule, lung containing the main nodule, and both lungs. A Convolutional Autoencoder was developed for the reconstruction of the input image. Thereafter, the encoder block was used as a feature extractor, stacking a classifier on top to assess the EGFR mutation status. Results showed that extending the analysis beyond the local nodule allowed the capture of more relevant information, suggesting the presence of useful biomarkers using the lung with nodule region of interest, which allowed to obtain the best prediction ability. This comparative study represents an innovative approach for gene mutations status assessment, contributing to the discussion on the extent of pathological phenomena associated with cancer development, and its contribution to more accurate Artificial Intelligence-based solutions, and constituting, to the best of our knowledge, the first deep learning approach that explores a comprehensive analysis for the EGFR mutation status classification.
A Quadratic High Step-up DC-DC Boost Converter Based on Coupled inductor with Single Switch and Continuous Input Current

In this paper, a novel quadratic high step-up DC-DC boost converter established on coupled inductor with single switch is introduced. The operation fundamentals of the presented converter are discussed and a simulation test has been carried out to verify validity of the presented converter. The presented converter is compared with other quadratic topologies in criteria as number of elements, voltage stress on switch and voltage gain. The proposed converter benefits from continuous input current and therefore, is applicable for renewable energy applications.
Plant Disease Detection and Classification by Deep Learning—A Review

Deep learning is a branch of artificial intelligence. In recent years, with the advantages of automatic learning and feature extraction, it has been widely concerned by academic and industrial circles. It has been widely used in image and video processing, voice processing, and natural language processing. At the same time, it has also become a research hotspot in the field of agricultural plant protection, such as plant disease recognition and pest range assessment, etc. The application of deep learning in plant disease recognition can avoid the disadvantages caused by artificial selection of disease spot features, make plant disease feature extraction more objective, and improve the research efficiency and technology transformation speed. This review provides the research progress of deep learning technology in the field of crop leaf disease identification in recent years. In this paper, we present the current trends and challenges for the detection of plant leaf disease using deep learning and advanced imaging techniques. We hope that this work will be a valuable resource for researchers who study the detection of plant diseases and insect pests. At the same time, we also discussed some of the current challenges and problems that need to be resolved.
Bounds on the Error Probability of Nonbinary Linear Codes over the Q-ary Symmetric Channel

The block error probability under maximum likelihood decoding of general linear block codes and code ensembles over the q-ary symmetric channel is analyzed. The analysis makes use of two types of upper bounds and it is based on the codes (code ensembles) distance spectra. The first bound generalizes Poltyrev's union bound for the binary symmetric channel. The second bound relies on typical pairs decoding. The two bounds are developed in an asymptotic setting to analyze the behavior of linear block code ensembles, in terms of error exponent and maximum-likelihood decoding threshold. A sphere-packing lower bound is also developed, to provide a reference for the finite-length performance.
Single-Frequency Amplitude-Modulation Sensor for Dielectric Characterization of Solids and Microfluidics

A microfluidic sensor based on a microstrip line loaded with a composite resonator is reported in this paper. The composite resonator combines a shunt connected step impedance resonator (SIR) and a complementary split ring resonator CSRR) etched in the ground plane. By etching the CSRR beneath the patch of the SIR, the composite CSRR-loaded SIR resonator exhibits two transmission zeros and a pole in between. The operating principle of the sensor is the variation of the transmission coefficient at the pole frequency of the bare resonator, when a material or liquid under test (LUT) is in contact with the CSRR (the sensitive element). By designing the CSRR-loaded SIR resonator with closely spaced pole and transmission zeros, highly sensitive sensors are obtained. Despite the fact that the proposed sensor can also operate as a frequency variation sensor, using it as a single-frequency sensor based on the variation of the transmission coefficient (caused by the LUT) at a specific frequency reduces sensor costs. The reason is that a harmonic signal injected to the input port of the microstrip-based sensor plus a simple amplitude modulation (AM) detector (connected to the output port) suffices for measuring purposes. The proposed microfluidic sensor is applied to the characterization of volume fraction of solutions of isopropanol in deionized (DI) water. The sensor is able to resolve volume fractions as small as 5%, and the maximum measured sensitivity is as good as 4 mV/%.
Dynamic Torso Compliance Control for Standing and Walking Balance of Position-Controlled Humanoid Robots

The humanoid robot, one of the ideal unmanned systems, has the potential to perform tasks currently carried out by human workers in industry, manufacturing, service, and disaster response. In such complicated environments with unknown disturbances, compliance is important for a humanoid robot, especially a position-controlled one, to maintain balance while performing a task. This article presents a dynamic torso compliance method for position-controlled humanoid robots to adapt to unknown external disturbances while standing or walking. This method comprises two independent controllers. One controller, which is based on a linear inverted pendulum with a flywheel, is used to realize compliant behavior by controlling the torso postures according to the zero moment point. The other controller, which is based on a double inverted pendulum, is adopted to obtain the dynamic torso movements as well as maintain balance through a quadratic-programming-based controller. With the proposed method, controls for compliance and balance are decoupled, and a position-controlled robot can perform dynamic torso compliance and maintain balance while standing or walking. The performance of the proposed method is validated by simulations and experiments on a position-controlled humanoid robot (BHR-T).
Automatic Hardware Trojan Generation Platforms in Integrated Circuits: A Critical Review

Hardware Trojan (HT) is a malicious program that can cause various kinds of effects related to sensitive information leakage, changes in computer functionality, and Denial of Service (DoS). Hardware Trojan Detection (HT Detection) is a tool developed during the process of making Integrated Circuits (ICs) to avoid anything that might be suspicious. Furthermore, different types of security mechanisms have been created to help and prevent HT from causing any damages. This paper aims to critically review current and previous HT detection approaches and comprehensively discuss the HT detection and prevention techniques that are attacking and triggering the system. Also, the current issues and challenges that arise and the approaches to addressing the issues will be gathered.
Research on Dynamic Taxi Ride-Sharing Price Model

With the diversity of people's travelling modes, the traditional taxi industry is facing severe challenges. In order to meet the travel requirements in the intelligent era, it is necessary to improve the market competitiveness of the taxi industry. This paper proposes a sharing pricing model based on the rate balancing between the driver and passenger. The main concept adopts the dynamic request method for the passengers to take the ride-sharing taxi. For the first passenger's request the nearest taxi receives the request, picks up the passenger on time and drives according to the shortest path calculated by Floyd algorithm. When other passengers ask for a drive, the driver will choose the passengers according to the taxi seat capacity to complete the ride-sharing process until all the passengers arrive at the destination. In the calculation of the driver's income and the passenger's costs we consider the driving conditions in different time periods and use the optimal solution of the model to make a sharing price. Finally the driver’s income and the passengers’ costs are compared and analyzed under three driving modes which are the common taxi, Didi express and ride-sharing taxi. The results show that the dynamic sharing pricing model designed in this paper is reasonable and very effective in daily life.
Robust Temporal Logic Motion Planning Based on Invariant Set Theory

In this paper, the problem of motion planning for a mobile robot is addressed in which motion planning scenarios such as reachability, invariance, surveillance and sequencing missions are formulated with Linear Temporal Logic (LTL). First, by using the idea of invariant set theory, a novel algorithm is proposed for checking the satisfaction of temporal logic planning specification in the offline phase. Then, an optimal control strategy is designed in the presence of bounded additive disturbances to meet the given temporal logic specification and system constraints. Illustrative case studies for a robot with a double integrator model are presented to demonstrate the applicability of the proposed approach in motion planning tasks.