Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Modal Component Embedding for Fake News Detection

As numerous fake news bloom and spread wildly on social media, fake news detection has recently been drawing a growing amount of attention. Single news consists of various multi-modal components (e.g., text, image, and event). Thus, a desirable model for fake news detection must satisfy two requirements: 1) it must correctly learn the reliability of each component 2) it must be capable of capturing the relationship among the components. In this paper, we propose a Multi-modal Component Embedding framework (MCE) for fake news detection, which is designed to satisfy all the requirements. It first defines a latent vector for each news article as the sum of its component latent vectors. For each component, we regard its magnitude as its reliability, and regard its directional relationship as its consistency. In this context, the magnitude of each news latent vector represents how reliable the news is. Thus, MCE learns the latent space so that the magnitude of the real news vectors becomes larger than that of the fake news vectors. During the training, a news vector becomes larger when its component vectors are reliable (i.e., large magnitude) and when its component vectors are well aligned (i.e., high consistency). By doing so, MCE can capture the complex relationship among the components as well as the reliability of each component. Our extensive experiments on two real-world datasets show that MCE outperforms all the baselines. We also provide a qualitative analysis on the embedding space to verify its capability of satisfying the requirements.
Scope for Deep Learning: A Study in Audio-Visual Speech Recognition

Audio-visual signal processing has been emerged as an important research area in multimodal signal processing. Among this, speech recognition from audio-visual signal is gained more attention because of unavailability of efficient acoustic noise removal methods. In this paper, the literature review on audio-visual speech recognition system has been presented. The different types of audio and visual feature extraction mechanisms are also discussed here. The various classification models have also been discussed along with this review.
Small-World Propensity Reveals the Frequency Specificity of Resting State Networks

Goal: Functional connectivity (FC) is an important indicator of the brain's state in different conditions, such as rest/task or health/pathology. Here we used high-density electroencephalography coupled to source reconstruction to assess frequency-specific changes of FC during resting state. Specifically, we computed the Small-World Propensity (SWP) index to characterize network small-world architecture across frequencies. Methods: We collected resting state data from healthy participants and built connectivity matrices maintaining the heterogeneity of connection strengths. For a subsample of participants, we also investigated whether the SWP captured FC changes after the execution of a working memory (WM) task. Results: We found that SWP demonstrated a selective increase in the alpha and low beta bands. Moreover, SWP was modulated by a cognitive task and showed increased values in the bands entrained by the WM task. Conclusions: SWP is a valid metric to characterize the frequency-specific behavior of resting state networks.
Steerable Sparse Linear Array Synthesis using Alternating Direction Method of Multipliers

In this paper, we present the Sparse Linear Array (SLA) synthesis using Alternating Directional Method of Multipliers (ADMM). The array has been synthesized for 2D Underwater Forward Looking Sonar (FLS) imaging system. The steerable SLA has been synthesized earlier using Multiple Measurement Vectors (MMV) Compressive Sensing (CS) method in far-field as well as in near-field. Synthesizing steerable SLA using MMV CS method for a high angular resolution imaging system with very fine resolution beam spacing pose challenges in terms of execution time and amount of requirement of the system memory. The ADMM algorithm includes steps which involve multiplication of vector and matrices and thus, reduces the execution time in synthesis of SLA and overcome the requirement of a very large system memory. The synthesized SLA using ADMM achieves similar sparsity and beampattern characteristics as achieved by MMV CS method and, allows to synthesize steerable SLA for very high angular resolution and fine beam spacing. We present the design of FLS operating at 300 kHz in far-field, 600 kHz and 1.2 MHz in near-field having angular resolution 0.86°, 0.44° and 0.22° in the broadside respectively. The beam spacing are 0.8°, 0.8° and l° in the field of view −30° to 30° for the SLA operating at 300 kHz, 600 kHz and 1.2 MHz. We also present the design of SLA operating at 1.2 MHz having angular resolution same as above but beam spacing is 0.25° instead 1°, which shows that the limitation imposed by MMV method in terms of large system memory has been overcome by ADMM method.
Yaw stability of 4WS with variable steering ratio strategy using inertial delay control based sliding mode control

In this paper Inertial delay control(IDC) based sliding mode control(SMC) for 4 wheel steering(4WS) vehicle which focuses on the maneuvering flexibility and steering stability of the vehicle. To achieve above a new method for variable steering ratio(VSR) is introduced. A nonlinear vehicle model is considered along with the uncertainties in cornering stiffness, mass and vehicle speed. To compensate the effect of nonlinearities and uncertainties and also controlling lateral dynamics an IDC based SMC is proposed to achieve required side-slip angle and yaw rate of vehicle based on VSR. Further a simulation is executed to analyze the performance of proposed controller for 4WS by tracking the system dynamics to follow the desired model regardless of the nonlinearities and uncertainties. The proposed controller is also validated with Carsim software.
Propelling motion modeling of an Hexapod robot

In this paper, we studied the motion of a hexapod robot taking into account its dynamics in Matlab’s SimMechanics. The software allows visualizing the moving system in Mechanics Explorers of Matlab with the possibility to measure many interesting physical terms. The kinematics and dynamics of the hexapod robot is well defined in the SimMechanics software as well as the functional schemes. We can thus connect them with the joints data elaborated from the kinematics modeling. This a powerful tool to have a deeper view in the dynamic behavior of the propelling motion of the hexapod robot. Therefore, we have been particularly interested in joint torques in response to specific propelling motion. This task was well established and validated by simulation. So, we can use all this information in the control process of the robot, a task that was too complex with the analytical approach.
An Improved Magnetostriction Model for Electrical Steel Sheet Based on Jiles–Atherton Model

The magnetostriction of an electrical steel sheet (ESS) is the main cause of the electromagnetically induced vibration of power transformers. In order to analyze the magnetostrictive hysteresis property of an ESS, a model is established based on the improved Jiles–Atherton (J–A) model by introducing the parameter $k_{1}$ , which considers the domain moment rotation. The magnetostrictive hysteretic loop of silicon steel would be obtained by combining the hysteretic loop with the model, in which the hysteretic loops can be more easily measured accurately than the magnetostriction loops. To verify the feasibility of the model, it is applied to Grain-oriented and non-oriented electrical steel, and the parameters in the model are extracted by using a particle swarm optimization algorithm. By comparing the measured data to calculate data obtained from the model, excellent agreement in the result is shown. The anisotropic properties of non-oriented silicon steel are also suitably shown. The results demonstrate the proposed model in the article is accurate for dynamic magnetic characteristics to electrical steel. It provides a good description of magnetostriction under the magnetic field with different amplitudes.
Coupling Soft Computing, Simulation and Optimization in Supply Chain Applications: Review and Taxonomy

Supply chain networks are typical examples of complex systems. Thereby, making decisions in such systems remains a very hard issue. To assist decision makers in formulating the appropriate strategies, robust tools are needed. Pure optimization models are not appropriate for several reasons. First, an optimization model cannot capture the dynamic behavior of a complex system. Furthermore, most common practical problems are very constrained to be modeled as simple tractable models. To fill in the gap, hybrid optimization/simulation techniques have been applied to improve the decision-making process. In this paper we explore the near-full spectrum of optimization methods and simulation techniques. A review and taxonomy were performed to give an overview of the broad field of optimization/simulation approaches applied to solve supply chain problems. Since the possibilities of coupling them are numerous, we launch a discussion and analysis that aims at determining the appropriate framework for the studied problem depending on its characteristics. Our study may serve as a guide for researchers and practitioners to select the suitable technique to solve a problem and/or to identify the promising issues to be further explored.
Speech Intelligence Using Machine Learning for Aphasia Individual

Aphasia is a neurological speech disorder which impairs the person’s ability to understand or express and also reading and writing capability of an Individual will be affected. Aphasia is caused due to stroke, brain trauma, tumor in cerebrum, brain surgery, infection in cerebrum, some neurological diseases. It also affects the language skills of an aphasia individual due to harmness in the section of cerebrum. Individuals with Aphasia face difficulties in sentence framing, analyzing meaning of words, finding correct words to express their ideas, feelings, thoughts and also problem with pronunciation and stammering, all these difficulties greatly affect their personal and professional life. This paper focus on bridging a gap between the individual with Aphasia and the receiver. This motivated us to come up with the Speech Intelligence System which act as an interface for the communication between the individual with Aphasia and the society. Based on the severity of Aphasia, our knowledge-based system will analyze the unstructured words pronounced by the speech disorder person and transformed it to the meaningful prediction Our Speech Intelligence System will interpret speech disorder person’s spoken words into text form by matching with the datasets.
Prioritizing Human Microbe-Disease Associations Utilizing a Node-Information-Based Link Propagation Method

Growing evidence shows that microbes in human body and body surface play critical roles in the development of many human diseases. Predicting the underlying associations between diseases and microbes is essential for deeply understanding the pathogenesis of diseases. However, biological experiments to find the relationship between microbes and diseases is usually laborious and time-consuming, which presents the need for effective computational tools. In this study, we propose a computational model of node-information-based Link Propagation for Human Microbe-Disease Association prediction (LPHMDA) to prioritize disease-related microbes. LPHMDA and 3 popular methods including KATZHMDA, PBHMDA, and LRLSHMDA were implemented and compared on the Human Microbe-Disease Association Database (HMDAD) based on cross-validation. As a result, LPHMDA achieved an AUC of 0.9135 in leave-one-out cross-validation (LOOCV), outperforming those of the 3 compared methods. In addition, the performances of LPHDMA on the 3-fold CV, 5-fold CV and 10-fold CV were also better than those of the other 3 canonical methods, further demonstrating its superiority. Finally, we took colorectal carcinoma, asthma and obesity as case studies. Interestingly, 9, 9 and 8 of the top 10 novel microbes predicted by LPHMDA to be associated with the 3 diseases respectively could be confirmed by literatures, providing potential disease-associated microbes for further experimental validation. In summary, LPHMDA is an effective method for prioritizing disease-associated microbes.
Research on Parameter Optimization of Potato Soil Transportation Separation Mechanism

In order to improve the operation effect of sweet potato harvester in China, this paper uses EDEM to analyze the single-factor simulation of the potato soil transport separation mechanism, and analyzes the simulation results by using the collision normal force and transport efficiency of sweet potato as the index parameters. The field experiment was designed to verify, and the test results were consistent with the simulation results, which proved the feasibility of using the discrete element method to simulate the operation process of the potato soil transport separation mechanism. However, the actual operation effect of the common potato soil transport separation mechanism is the result of the joint influence of multiple parameters. Therefore, based on the EDEM-based basic simulation, the collision normal force and the transport efficiency are the optimization objectives, and the orthogonal simulation experiment with interaction is used to optimize the parameters, and two sets of parameter optimization combinations are obtained. Based on the secondary development of EDEM software, the number of bond breaks and the change trend of large soil particles were used as evaluation indexes to simulate the two sets of working parameters, and finally the optimal parameters were obtained. The combination is a bar lift with a line speed of 2.3 m/s, an amplitude of 8 mm, and an inclination of 18°.
Enhanced Text Matching Based on Semantic Transformation

Text matching is the core of natural language processing (NLP) system. It’s considered as a touchstone of the NLP, and it aims to find whether text pairs are equal in semantics. However, the semantic gap in text matching is still an open problem to solve. Inspired by successes of cycle-consistent adversarial network (CycleGAN) in image domain transformation, we propose an enhanced text matching method based on the CycleGAN combined with the Transformer network. Based on the proposed method, the text semantics in a source domain is transferred to a similar or different target domain, and the semantic distance between text pairs is decreased. Meanwhile, we demonstrate our method in paraphrase identification and question answer matching. The matching degree is computed by a standard text matching model to evaluate the transforming influence on narrowing the text semantic gap. The experiments show that our method achieves text domain adaptation, and the effects on different matching models are remarkable.
Asymmetric Multilevel Inverter Induction Motor Drive for Fuel Cell Based Electrical Vehicles

This paper presents direct torque control scheme for an asymmetric multilevel inverter fed induction motor drive for fuel cell based electrical vehicles. The controlling principle is based on Direct torque control (DTC) method. The torque and flux estimator takes input as measured voltage, current and stator resistance. The estimator further calculates the error in flux and torque. The SPWM generates such switching signals which reduce error. The asymmetric multilevel inverter provides almost sinusoidal voltage with less total harmonics distortion (THD) and less switching devices resulting in lesser switching losses. In addition, torque ripple is minimized because of small dv/dt, since each power cell of multilevel inverter operates at switching rate which is very high so we obtain an efficient torque and flux controller. A comparative analysis of two level and multilevel inverter is also done in respect of transient responses and torque ripple. To authenticate the accomplishment of DTC for an asymmetric multilevel induction motor drive for fuel cell electrical vehicles, Simulations are executed in MATLAB/Simulink. The results vindicate advantages of asymmetrical multilevel induction motor drive DTC control for electrical vehicles.
Image Based Emotion Detection

Human emotion is one of the fundamental ways we express our feelings and currently the main focus is only facial expression for emotion extraction from an image. The aim of this paper is to enlighten the importance of other different features of an image in extracting the emotions apart from only facial features. The created system is able to accept an image uploaded by the user and detect the emotion individually. Firstly, the emotion is predicted based on the facial feature, then by the color and then by the text. Lastly, all three detected emotion is put to a comparison and a final output is displayed to the user. The created system went through an evaluation and the results were striking to see that the system prediction was often similar to the human prediction and the users were satisfied with the outcome.
Remaining Useful Life Prediction of Machining Tools by 1D-CNN LSTM Network

In the field of machining, machining tool life (degree of wear) is a key factor affecting the quality of the machined workpiece. Over-protection strategies may increase production costs and cause unnecessary machining tool downtime. Therefore, if the remaining useful life (RUL) of the machining tool can be accurately predicted, the work schedule will be effectively optimized and the machining tool procurement cost will be reduced. In this paper, we propose a system schema that integrates programmable logic controller (PLC) signals with sensor signals for online RUL prediction of machining tools. The preprocessed sensor signals are segmented and we propose ensemble discrete wavelets transform (EDWT) to eliminate the noise of three-dimensional vibration signals and get time- frequency information. Then statistics features are extracted based on time domain and frequency domain analysis. Further, we use spearman’s coefficient, autocorrelation and monotonicity indicators for feature selection to reduce feature dimensions. Finally, we use a 1D-CNN LSTM network architecture for machining tools RUL prediction. The evaluation results show that our system schema is feasible for the industrial field, and has a better performance than other common methods.
Non Orthogonal Multiple Access in Cooperative Relay Sharing with Randomly Deployed Users

We have investigated and proposed a cooperative communicating strategy using non-orthogonal multiple access (NOMA) in which the users are randomly deployed. In this technique there are two randomly deployed sources which share or exchange information with their respective destinations parallely over the same range of frequency through a common shared relay. Further in this strategy, after the symbols are received they are transmitted simultaneously in parallel by both the sources with different powers allocated, the relay sends a coded complex signal super-positioned using NOMA-RS to the respective destinations. The most important benefit of NOMA-RS is that same relay can be shared by two sources. Through the mathematical calculations and simulations, we demonstrate the efficiency of the suggested strategy in terms of outage probability of two source and destination pairs.
Leveraging Linear Programming for Deployment of Container-based Applications within Softwarized Networks in Fog Computing

Softwarization and virtualization of both computing resources and networking assets have made deployment-related procedures more flexible. In this paper, it is explored how to leverage optimization techniques based on linear programming for optimal deployment and network traffic shaping under the given constraints. A model is proposed, implemented in AMPL mathematical programming language and illustrated with example scenarios in context of Fog Computing.
A Novel Assist-As-Needed Controller Based on Fuzzy-Logic Inference and Human Impedance Identification for Upper-Limb Rehabilitation

Upper-limb rehabilitation robots are being increasingly included in the rehabilitation after stroke. Clinical studies suggest that the patient’s active participation can maximize effectiveness of robot-assisted rehabilitation and result in fastest possible recovery. In order to promote active involvement of the patient, we develop a subject-adaptive controller within the assist-as-needed (AAN) paradigm. The controller employs Recursive Least Square (RLS) algorithm to identify human arm impedance parameters, with aims to quantify the residual motor capability of the patient. According to the upper-limb impedance of the patient in the direction along movements, the reference trajectory can be generated based on the motion patterns in healthy humans. In addition, the fuzzy logic control strategy is implemented in the direction perpendicular to movements, which is capable of adjusting the robotic assistance level by considering both upper-limb impedance of the patient and the variations of deviation between the reference trajectory and actual trajectory. Furthermore, the proposed subject-adaptive AAN controller was validated in the simulation study, and the results demonstrated the capability of the controller to modulate the assistance level in accordance with the subject’s instantaneous requirements. Future works will focus on the implementation of the control scheme with post-stroke patients.
Artificial Neural Networks Optimized with Unsupervised Clustering for IDS Classification

Information systems are becoming more and more complex and closely linked. These systems are encountering an enormous amount of nefarious traffic while ensuring real - time connectivity. Therefore, a defense method needs to be in place. One of the commonly used tools for network security is intrusion detection systems (IDS). An IDS tries to identify fraudulent activity using predetermined signatures or pre-established user misbehavior while monitoring incoming traffic. Intrusion detection systems based on signature and behavior cannot detect new attacks and fall when small behavior deviations occur. Many researchers have proposed various approaches to intrusion detection using machine learning techniques as a new and promising tool to remedy this problem. In this paper, the authors present a combination of two machine learning methods, unsupervised clustering followed by a supervised classification framework as a Fast, highly scalable and precise packets classification system. This model’s performance is assessed on the new proposed dataset by the Canadian Institute for Cyber security and the University of New Brunswick (CICIDS2017). The overall process was fast, showing high accuracy classification results.